diff --git a/app/dependencies/__init__.py b/app/dependencies/__init__.py
new file mode 100644
index 0000000..8fdda1c
--- /dev/null
+++ b/app/dependencies/__init__.py
@@ -0,0 +1,11 @@
+# app/dependencies/__init__.py
+
+from datetime import datetime
+
+# 글로벌 변수 초기화
+globals_dict = {}
+model_initializing = False  # 모델 초기화 상태를 추적하는 전역 변수
+last_initialization_attempt = None  # 마지막 초기화 시도 시간
+
+def get_globals_dict():
+    return globals_dict
\ No newline at end of file
diff --git a/app/router/evaluation.py b/app/router/evaluation.py
new file mode 100644
index 0000000..edda854
--- /dev/null
+++ b/app/router/evaluation.py
@@ -0,0 +1,166 @@
+# app/routers/evaluation.py
+
+from fastapi import APIRouter, Depends, HTTPException
+from app.schema.recommendation_schema import RecommendationEvaluationResponse
+from app.services.evaluation.evaluator import evaluate_recommendation_model, evaluate_with_cross_validation
+from app.services.evaluation.diversity_metrics import evaluate_recommendation_diversity
+from app.services.model_trainer.hyperparameter_tuning import optimize_recommendation_parameters
+from app.dependencies import get_globals_dict
+from app.services.model_trainer import evaluate_model, optimize_recommendation_parameters 
+
+# 각 알고리즘별 결과 평가
+from app.services.model_trainer.recommenation.basic import generate_recommendations
+from app.services.model_trainer.recommenation.hybrid import generate_hybrid_recommendations
+
+import json
+import logging
+import numpy as np
+
+router = APIRouter(
+    prefix="/evaluate",
+    tags=["evaluation"],
+    responses={404: {"description": "Not found"}},
+)
+
+logger = logging.getLogger(__name__)
+
+@router.get("/basic", response_model=RecommendationEvaluationResponse)
+async def evaluate_basic(globals_dict=Depends(get_globals_dict)):
+    """기본 추천 시스템 평가 수행"""
+    try:
+        metrics = evaluate_recommendation_model(globals_dict)
+        
+        # 추가 다양성 지표 계산
+        df_model = globals_dict.get("df_model")
+        
+        if "test_interactions" in globals_dict and df_model is not None:
+            test_interactions = globals_dict.get("test_interactions")
+            
+            # 추천 결과를 사용자-식당 매핑 딕셔너리로 변환
+            recommendations_dict = {}
+            for user_id, metrics in globals_dict.get("recommendations_results", {}).items():
+                recommendations_dict[user_id] = [r['restaurant_id'] for r in metrics.get('recommendations', [])]
+            
+            diversity_metrics = evaluate_recommendation_diversity(
+                recommendations_dict, 
+                user_history=test_interactions, 
+                restaurant_data=df_model
+            )
+            
+            # 기본 지표와 다양성 지표 병합
+            metrics.update(diversity_metrics)
+        
+        return {"metrics": metrics, "status": "success"}
+    
+    except Exception as e:
+        logger.error(f"평가 중 오류 발생: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.get("/cross-validation", response_model=RecommendationEvaluationResponse)
+async def evaluate_with_cross_val(n_splits: int = 5, globals_dict=Depends(get_globals_dict)):
+    """교차 검증 평가 수행"""
+    try:
+        metrics = evaluate_with_cross_validation(globals_dict, n_splits=n_splits)
+        return {"metrics": metrics, "status": "success"}
+    
+    except Exception as e:
+        logger.error(f"교차 검증 평가 중 오류 발생: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.post("/optimize-params", response_model=dict)
+async def optimize_parameters(n_trials: int = 30, timeout: int = 300, globals_dict=Depends(get_globals_dict)):
+    """추천 시스템 파라미터 최적화"""
+    try:
+        df_model = globals_dict.get("df_model")
+        user_features_df = globals_dict.get("user_features_df")
+        model_features = globals_dict.get("model_features")
+        
+        if df_model is None or user_features_df is None or model_features is None:
+            raise HTTPException(status_code=400, detail="필요한 데이터가 로드되지 않았습니다.")
+        
+        best_params = optimize_recommendation_parameters(
+            df_model, 
+            user_features_df, 
+            model_features, 
+            n_trials=n_trials,
+            timeout=timeout
+        )
+        
+        return {"best_parameters": best_params, "status": "success"}
+    
+    except Exception as e:
+        logger.error(f"파라미터 최적화 중 오류 발생: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.get("/compare-algorithms", response_model=dict)
+async def compare_algorithms(globals_dict=Depends(get_globals_dict)):
+    """서로 다른 추천 알고리즘 비교"""
+    try:
+        df_model = globals_dict.get("df_model")
+        user_features_df = globals_dict.get("user_features_df")
+        
+        if df_model is None or user_features_df is None:
+            raise HTTPException(status_code=400, detail="필요한 데이터가 로드되지 않았습니다.")
+        
+        # 샘플 사용자 선택
+        sample_users = df_model['user_id'].sample(min(20, df_model['user_id'].nunique())).unique()
+        
+        basic_metrics = {}
+        hybrid_metrics = {}
+        
+        for user_id in sample_users:
+            # 기본 추천 알고리즘 평가
+            stacking_reg = globals_dict.get("stacking_reg")
+            scaler = globals_dict.get("scaler")
+            model_features = globals_dict.get("model_features")
+            
+            if all([stacking_reg, scaler, model_features]):
+                # 기본 추천 생성
+                basic_result = json.loads(generate_recommendations(
+                    df_model.copy(),
+                    stacking_reg,
+                    model_features,
+                    user_id,
+                    scaler,
+                    user_features=user_features_df
+                ))
+                
+                # 하이브리드 추천 생성
+                hybrid_result = generate_hybrid_recommendations(
+                    df_model.copy(),
+                    df_model.copy(),
+                    user_id,
+                    n=15,
+                    alpha=0.7
+                )
+                
+                # 평가 지표 계산 및 저장 (여기서는 간소화를 위해 추천된 식당 수만 계산)
+                basic_metrics[user_id] = {
+                    'num_recommendations': len(basic_result.get('recommendations', [])),
+                    'is_new_user': basic_result.get('is_new_user', True)
+                }
+                
+                hybrid_metrics[user_id] = {
+                    'num_recommendations': len(hybrid_result.get('recommendations', [])),
+                    'is_new_user': hybrid_result.get('is_new_user', True)
+                }
+        
+        # 결과 정리
+        result = {
+            'basic_algorithm': {
+                'avg_recommendations': np.mean([m['num_recommendations'] for m in basic_metrics.values()]),
+                'new_user_ratio': np.mean([m['is_new_user'] for m in basic_metrics.values()]),
+                'coverage': len(basic_metrics) / len(sample_users) if sample_users.size > 0 else 0
+            },
+            'hybrid_algorithm': {
+                'avg_recommendations': np.mean([m['num_recommendations'] for m in hybrid_metrics.values()]),
+                'new_user_ratio': np.mean([m['is_new_user'] for m in hybrid_metrics.values()]),
+                'coverage': len(hybrid_metrics) / len(sample_users) if sample_users.size > 0 else 0
+            }
+        }
+        
+        return {"comparison_results": result, "status": "success"}
+    
+    except Exception as e:
+        logger.error(f"알고리즘 비교 중 오류 발생: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
\ No newline at end of file
diff --git a/app/router/recommendation_api.py b/app/router/recommendation_api.py
index 268c878..6536030 100644
--- a/app/router/recommendation_api.py
+++ b/app/router/recommendation_api.py
@@ -11,8 +11,11 @@
 from app.services.preprocess.restaurant.data_loader import load_restaurant_json_files, load_user_json_files
 from app.services.preprocess.restaurant.preprocessor import preprocess_data
 from app.services.model_trainer import train_model
-from app.services.model_trainer.recommendation import generate_recommendations
+from app.services.model_trainer.recommenation.basic import generate_recommendations
 from app.services.preprocess.user.user_preprocess import user_preprocess_data  # 사용자 데이터 전처리 모듈 추가
+from app.services.evaluation.evaluator import evaluate_recommendation_model
+from app.services.model_trainer import train_model, optimize_recommendation_parameters 
+from app.dependencies import globals_dict, model_initializing, last_initialization_attempt
 from typing import List, Dict, Any
 from datetime import datetime
 
@@ -20,11 +23,6 @@
 logger = logging.getLogger("recommendation_api")
 router = APIRouter()
 
-# 글로벌 변수 초기화
-globals_dict = {}
-model_initializing = False  # 모델 초기화 상태를 추적하는 전역 변수
-last_initialization_attempt = None  # 마지막 초기화 시도 시간
-
 # 초기 데이터 로딩 및 모델 학습
 def initialize_model(force=False):
     global globals_dict, model_initializing, last_initialization_attempt
@@ -173,6 +171,32 @@ async def check_model_status():
     
     return status
 
+# 평가 지표 확인 엔드포인트 추가
+@router.get("/evaluate", response_model=Dict[str, Any])
+async def evaluate_model():
+    """현재 추천 모델의 성능 지표를 계산합니다."""
+    global globals_dict, model_initializing
+    
+    try:
+        # 모델 초기화 상태 확인
+        if not globals_dict or "stacking_reg" not in globals_dict or "df_model" not in globals_dict:
+            raise HTTPException(
+                status_code=503, 
+                detail="모델이 초기화되지 않았습니다. 먼저 모델을 초기화하세요.",
+                headers={"Retry-After": "30"}
+            )
+        
+        # 모델 평가
+        metrics = evaluate_recommendation_model(globals_dict)
+        
+        return metrics
+    
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"모델 평가 중 오류 발생: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
+
 # recommend 함수 내부 수정
 @router.post("",
              response_model=Dict[str, Any],
diff --git a/app/schema/recommendation_schema.py b/app/schema/recommendation_schema.py
index 4983d21..6734de1 100644
--- a/app/schema/recommendation_schema.py
+++ b/app/schema/recommendation_schema.py
@@ -1,7 +1,7 @@
 # app/models/recommendation_schema.py
 
 from pydantic import BaseModel, Field
-from typing import List, Annotated
+from typing import Dict, Any, List, Optional, Annotated
 
 class RecommendationItem(BaseModel):
     category_id: int
@@ -30,3 +30,15 @@ class UserData(BaseModel):
     user_id: str
     # 선호 카테고리는 최소 1개, 최대 3개 (Pydantic v2 방식)
     preferred_categories: Annotated[List[str], Field(min_length=1, max_length=3)]
+
+class RecommendationEvaluationResponse(BaseModel):
+    metrics: Dict[str, Any]
+    status: str
+
+class HyperparameterOptimizationRequest(BaseModel):
+    n_trials: Optional[int] = 30
+    timeout: Optional[int] = 300
+
+class HyperparameterOptimizationResponse(BaseModel):
+    best_parameters: Dict[str, Any]
+    status: str
\ No newline at end of file
diff --git a/app/services/evaluation/__init__.py b/app/services/evaluation/__init__.py
new file mode 100644
index 0000000..7f50127
--- /dev/null
+++ b/app/services/evaluation/__init__.py
@@ -0,0 +1,19 @@
+# app/services/evaluation/__init__.py
+
+from app.services.evaluation.evaluator import (
+    evaluate_recommendation_model,
+    evaluate_with_cross_validation
+)
+from app.services.evaluation.data_generation import (
+    create_test_interactions,
+    create_stratified_train_test_split
+)
+from app.services.evaluation.utils import default_empty_metrics
+
+__all__ = [
+    'evaluate_recommendation_model',
+    'evaluate_with_cross_validation',
+    'create_test_interactions',
+    'create_stratified_train_test_split',
+    'default_empty_metrics'
+]
\ No newline at end of file
diff --git a/app/services/evaluation/data_generation.py b/app/services/evaluation/data_generation.py
new file mode 100644
index 0000000..1a0c194
--- /dev/null
+++ b/app/services/evaluation/data_generation.py
@@ -0,0 +1,220 @@
+# app/services/evaluation/data_generation.py
+
+import logging
+import numpy as np
+import pandas as pd
+
+logger = logging.getLogger(__name__)
+
+def create_test_interactions(globals_dict):
+    """
+    더 현실적인 사용자-식당 상호작용 테스트 데이터 생성
+    
+    Args:
+        globals_dict: 전역 변수 딕셔너리
+    
+    Returns:
+        pd.DataFrame: 상호작용 테스트 데이터
+    """
+    try:
+        df_model = globals_dict.get("df_model")
+        user_features_df = globals_dict.get("user_features_df")
+        
+        if df_model is None or user_features_df is None:
+            logger.warning("테스트 데이터 생성에 필요한 데이터가 부족합니다.")
+            return pd.DataFrame(columns=['user_id', 'restaurant_id', 'score'])
+        
+        # 모든 사용자와 식당 데이터 가져오기
+        all_users = user_features_df['user_id'].tolist()
+        all_restaurants = df_model['restaurant_id'].unique().tolist()
+        
+        # 사용자별 프로필 특성 (있다면) 활용
+        user_preferences = {}
+        
+        if 'preferred_category' in user_features_df.columns:
+            for _, user_row in user_features_df.iterrows():
+                user_id = user_row['user_id']
+                preferred_category = user_row.get('preferred_category')
+                user_preferences[user_id] = preferred_category
+        
+        # 테스트 상호작용 생성
+        test_interactions = []
+        
+        for user_id in all_users[:50]:  # 테스트를 위해 50명 사용자만 선택
+            # 사용자별 특성에 맞는 식당 선택
+            user_preferred_category = user_preferences.get(user_id)
+            
+            if user_preferred_category:
+                # 사용자 선호 카테고리가 있으면 해당 카테고리의 식당들 중에서 선택
+                category_restaurants = df_model[
+                    df_model['category_id'] == user_preferred_category
+                ]['restaurant_id'].tolist()
+                
+                if category_restaurants:
+                    # 선호 카테고리 식당 선택 (60%)
+                    preferred_count = max(1, int(np.random.randint(3, 6) * 0.6))
+                    preferred_restaurants = np.random.choice(
+                        category_restaurants, 
+                        size=min(preferred_count, len(category_restaurants)),
+                        replace=False
+                    )
+                    
+                    # 기타 랜덤 식당 선택 (40%)
+                    other_restaurants = list(set(all_restaurants) - set(category_restaurants))
+                    other_count = np.random.randint(1, 3)
+                    other_selected = np.random.choice(
+                        other_restaurants,
+                        size=min(other_count, len(other_restaurants)),
+                        replace=False
+                    )
+                    
+                    selected_restaurants = np.concatenate([preferred_restaurants, other_selected])
+                else:
+                    # 선호 카테고리 식당이 없으면 랜덤 선택
+                    selected_restaurants = np.random.choice(
+                        all_restaurants, 
+                        size=np.random.randint(3, 6),
+                        replace=False
+                    )
+            else:
+                # 사용자 선호도 정보가 없으면 랜덤 선택
+                selected_restaurants = np.random.choice(
+                    all_restaurants, 
+                    size=np.random.randint(3, 6),
+                    replace=False
+                )
+            
+            # 선택된 식당에 대한 가상 평점 생성 (3.0 ~ 5.0)
+            for restaurant_id in selected_restaurants:
+                # 선호 카테고리 식당은 더 높은 평점 확률
+                if user_preferred_category and restaurant_id in category_restaurants:
+                    score = np.random.uniform(4.0, 5.0)  # 선호 카테고리 식당은 평점 높게
+                else:
+                    score = np.random.uniform(3.0, 5.0)  # 기타 식당은 일반적인 분포
+                
+                test_interactions.append({
+                    'user_id': user_id,
+                    'restaurant_id': restaurant_id,
+                    'score': round(score, 1)  # 소수점 첫째 자리까지
+                })
+        
+        return pd.DataFrame(test_interactions)
+    
+    except Exception as e:
+        logger.error(f"테스트 데이터 생성 중 오류: {e}", exc_info=True)
+        return pd.DataFrame(columns=['user_id', 'restaurant_id', 'score'])
+
+def create_stratified_train_test_split(df_model, test_ratio=0.2, random_state=42):
+    """
+    사용자와 식당 분포를 보존하는 층화 훈련/테스트 분할
+    
+    Args:
+        df_model (DataFrame): 분할할 데이터프레임
+        test_ratio (float): 테스트 데이터 비율 (0~1)
+        random_state (int): 랜덤 시드
+    
+    Returns:
+        tuple: (훈련 데이터프레임, 테스트 데이터프레임)
+    """
+    try:
+        from sklearn.model_selection import GroupShuffleSplit
+        
+        logger.debug("층화 데이터 분할 시작...")
+        
+        # 필수 컬럼 확인
+        required_cols = ['user_id', 'restaurant_id']
+        for col in required_cols:
+            if col not in df_model.columns:
+                logger.error(f"데이터에 필수 컬럼이 없습니다: {col}")
+                raise ValueError(f"데이터에 필수 컬럼이 없습니다: {col}")
+        
+        # 사용자별 데이터 분할
+        users = df_model['user_id'].unique()
+        logger.info(f"총 {len(users)}명의 사용자 데이터에 대해 분할 수행")
+        
+        # 사용자별 활동 수준 파악 (상호작용 횟수 기준)
+        user_activity = df_model.groupby('user_id').size()
+        low_activity = user_activity[user_activity <= 5].index.tolist()
+        medium_activity = user_activity[(user_activity > 5) & (user_activity <= 20)].index.tolist()
+        high_activity = user_activity[user_activity > 20].index.tolist()
+        
+        logger.debug(f"저활동 사용자: {len(low_activity)}명, 중활동: {len(medium_activity)}명, 고활동: {len(high_activity)}명")
+        
+        # 각 활동 그룹별로 층화 분할
+        train_indices = []
+        test_indices = []
+        
+        for activity_group in [low_activity, medium_activity, high_activity]:
+            if not activity_group:
+                continue
+                
+            # 해당 활동 그룹의 사용자 데이터만 선택
+            group_data = df_model[df_model['user_id'].isin(activity_group)]
+            group_users = group_data['user_id'].unique()
+            
+            # 사용자를 훈련/테스트로 분할
+            np.random.seed(random_state)
+            test_size = int(len(group_users) * test_ratio)
+            test_users = np.random.choice(group_users, size=test_size, replace=False)
+            train_users = np.array([u for u in group_users if u not in test_users])
+            
+            # 각 사용자별 데이터 인덱스 수집
+            for user in train_users:
+                user_indices = group_data[group_data['user_id'] == user].index.tolist()
+                
+                # 활동량이 많은 사용자는 일부 데이터만 훈련에 사용
+                if user in high_activity and len(user_indices) > 30:
+                    np.random.shuffle(user_indices)
+                    train_indices.extend(user_indices[:30])  # 최대 30개 상호작용만 사용
+                else:
+                    train_indices.extend(user_indices)
+            
+            for user in test_users:
+                user_indices = group_data[group_data['user_id'] == user].index.tolist()
+                
+                # 테스트용 사용자도 최대 10개 상호작용만 테스트에 사용
+                if len(user_indices) > 10:
+                    np.random.shuffle(user_indices)
+                    test_indices.extend(user_indices[:10])
+                else:
+                    test_indices.extend(user_indices)
+        
+        # 훈련/테스트 데이터프레임 생성
+        train_df = df_model.loc[train_indices].copy()
+        test_df = df_model.loc[test_indices].copy()
+        
+        # 균형 확인
+        logger.info(f"훈련 데이터: {len(train_df)} 상호작용, {train_df['user_id'].nunique()} 사용자")
+        logger.info(f"테스트 데이터: {len(test_df)} 상호작용, {test_df['user_id'].nunique()} 사용자")
+        
+        # 점수(score) 컬럼이 없는 경우 가상의 점수 생성 (평가를 위해)
+        if 'score' not in test_df.columns:
+            logger.warning("테스트 데이터에 'score' 컬럼이 없어 가상 점수를 생성합니다")
+            
+            # 카테고리별 평균 점수 계산 (카테고리 정보가 있는 경우)
+            if 'category_id' in test_df.columns and 'category_id' in train_df.columns:
+                category_avg_scores = train_df.groupby('category_id')['score'].mean().to_dict()
+                default_score = train_df['score'].mean()
+                
+                # 카테고리 기반 가상 점수 할당
+                test_df['score'] = test_df['category_id'].map(
+                    lambda x: category_avg_scores.get(x, default_score)
+                )
+                
+                # 약간의 무작위성 추가 (±0.5)
+                np.random.seed(random_state)
+                test_df['score'] += np.random.uniform(-0.5, 0.5, size=len(test_df))
+                test_df['score'] = test_df['score'].clip(1, 5)  # 1~5 범위로 제한
+            else:
+                # 카테고리 정보가 없으면 3~5 사이 무작위 점수
+                test_df['score'] = np.random.uniform(3, 5, size=len(test_df))
+        
+        logger.debug("층화 데이터 분할 완료")
+        return train_df, test_df
+        
+    except Exception as e:
+        logger.error(f"층화 데이터 분할 중 오류 발생: {e}", exc_info=True)
+        # 기본 분할 방식 적용
+        from sklearn.model_selection import train_test_split
+        train_df, test_df = train_test_split(df_model, test_size=test_ratio, random_state=random_state)
+        return train_df, test_df
\ No newline at end of file
diff --git a/app/services/evaluation/diversity_metrics.py b/app/services/evaluation/diversity_metrics.py
new file mode 100644
index 0000000..f9ef416
--- /dev/null
+++ b/app/services/evaluation/diversity_metrics.py
@@ -0,0 +1,201 @@
+# app/services/evaluation/diversity_metrics.py
+
+import numpy as np
+import pandas as pd
+import scipy.stats
+import logging
+
+logger = logging.getLogger(__name__)
+
+def evaluate_recommendation_diversity(recommendations, user_history=None, restaurant_data=None):
+    """
+    추천 결과의 다양성을 평가하는 함수
+    
+    Args:
+        recommendations: 사용자별 추천 결과 딕셔너리 {user_id: [restaurant_ids]}
+        user_history: 사용자 과거 상호작용 데이터 (옵션)
+        restaurant_data: 식당 메타데이터 (옵션)
+    
+    Returns:
+        dict: 다양성 평가 결과
+    """
+    try:
+        diversity_metrics = {}
+        
+        # 추천 결과를 DataFrame으로 변환
+        recommendations_list = []
+        for user_id, rest_ids in recommendations.items():
+            for rank, rest_id in enumerate(rest_ids):
+                recommendations_list.append({
+                    'user_id': user_id,
+                    'restaurant_id': rest_id,
+                    'rank': rank + 1
+                })
+        
+        recs_df = pd.DataFrame(recommendations_list)
+        if recs_df.empty:
+            logger.warning("추천 결과가 없어 다양성 평가를 진행할 수 없습니다.")
+            return {
+                "category_diversity": 0,
+                "user_coverage": 0,
+                "item_coverage": 0,
+                "novelty": 0,
+                "serendipity": 0,
+                "intra_list_similarity": 0
+            }
+        
+        # 1. 카테고리 다양성 평가
+        if restaurant_data is not None and 'category_id' in restaurant_data.columns:
+            # 식당별 카테고리 매핑
+            restaurant_categories = restaurant_data.set_index('restaurant_id')['category_id'].to_dict()
+            
+            # 추천된 식당에 카테고리 매핑
+            recs_df['category_id'] = recs_df['restaurant_id'].map(restaurant_categories)
+            
+            # 카테고리 엔트로피 계산
+            if 'category_id' in recs_df.columns and not recs_df['category_id'].isna().all():
+                category_counts = recs_df['category_id'].value_counts(normalize=True)
+                category_entropy = scipy.stats.entropy(category_counts)
+                category_diversity = 1 - (1 / (1 + category_entropy))  # 0~1 범위로 정규화
+                diversity_metrics['category_diversity'] = category_diversity
+            else:
+                diversity_metrics['category_diversity'] = 0
+        else:
+            diversity_metrics['category_diversity'] = 0
+        
+        # 2. 사용자 커버리지 (전체 사용자 중 추천을 받은 사용자 비율)
+        if user_history is not None:
+            all_users = user_history['user_id'].unique()
+            recommended_users = set(recs_df['user_id'].unique())
+            user_coverage = len(recommended_users) / len(all_users) if len(all_users) > 0 else 0
+            diversity_metrics['user_coverage'] = user_coverage
+        else:
+            diversity_metrics['user_coverage'] = 1.0  # 사용자 히스토리가 없으면 100%로 가정
+        
+        # 3. 아이템 커버리지 (전체 식당 중 추천된 식당 비율)
+        if restaurant_data is not None:
+            all_restaurants = restaurant_data['restaurant_id'].unique()
+            recommended_restaurants = set(recs_df['restaurant_id'].unique())
+            item_coverage = len(recommended_restaurants) / len(all_restaurants) if len(all_restaurants) > 0 else 0
+            diversity_metrics['item_coverage'] = item_coverage
+        else:
+            diversity_metrics['item_coverage'] = 0
+        
+        # 4. 새로움 (Novelty) - 인기 있는 아이템보다 덜 알려진 아이템 추천 정도
+        if user_history is not None and restaurant_data is not None:
+            # 식당별 인기도 (상호작용 횟수) 계산
+            restaurant_popularity = user_history['restaurant_id'].value_counts()
+            total_interactions = len(user_history)
+            
+            # 각 식당의 희소성 = -log(인기도)
+            restaurant_rarity = restaurant_popularity.map(
+                lambda x: -np.log(x / total_interactions) if x > 0 else 0
+            )
+            
+            # 추천된 식당의 평균 희소성 (높을수록 새로운 항목을 추천)
+            recommended_rarity = []
+            for rest_id in recs_df['restaurant_id'].unique():
+                if rest_id in restaurant_rarity:
+                    recommended_rarity.append(restaurant_rarity[rest_id])
+            
+            novelty = np.mean(recommended_rarity) if recommended_rarity else 0
+            
+            # 0~1 범위로 정규화 (높을수록 새로움이 큼)
+            max_possible_novelty = -np.log(1 / total_interactions) if total_interactions > 0 else 1
+            normalized_novelty = novelty / max_possible_novelty if max_possible_novelty > 0 else 0
+            
+            diversity_metrics['novelty'] = normalized_novelty
+        else:
+            diversity_metrics['novelty'] = 0
+        
+        # 5. 세렌디피티 (Serendipity) - 의외성, 예상치 못한 발견
+        if user_history is not None and restaurant_data is not None:
+            # 사용자별 과거 상호작용 카테고리
+            user_categories = {}
+            for user_id in recs_df['user_id'].unique():
+                user_hist = user_history[user_history['user_id'] == user_id]
+                user_rest_ids = user_hist['restaurant_id'].tolist()
+                
+                # 해당 식당들의 카테고리 추출
+                user_rest_categories = [
+                    restaurant_categories.get(rest_id) 
+                    for rest_id in user_rest_ids 
+                    if rest_id in restaurant_categories
+                ]
+                
+                user_categories[user_id] = set(user_rest_categories)
+            
+            # 각 사용자의 추천 중 새로운 카테고리 비율 계산
+            serendipity_scores = []
+            
+            for user_id in recs_df['user_id'].unique():
+                if user_id not in user_categories:
+                    continue
+                    
+                user_recs = recs_df[recs_df['user_id'] == user_id]
+                
+                # 추천된 식당들의 카테고리
+                rec_categories = [
+                    restaurant_categories.get(rest_id) 
+                    for rest_id in user_recs['restaurant_id'] 
+                    if rest_id in restaurant_categories
+                ]
+                
+                # 사용자가 과거에 경험하지 않은 카테고리 비율
+                new_categories = [cat for cat in rec_categories if cat not in user_categories[user_id]]
+                serendipity = len(new_categories) / len(rec_categories) if rec_categories else 0
+                
+                serendipity_scores.append(serendipity)
+            
+            # 전체 사용자의 평균 세렌디피티
+            diversity_metrics['serendipity'] = np.mean(serendipity_scores) if serendipity_scores else 0
+        else:
+            diversity_metrics['serendipity'] = 0
+        
+        # 6. 추천 목록 내 유사성 (낮을수록 다양한 추천)
+        if restaurant_data is not None and 'category_id' in restaurant_data.columns:
+            intra_similarities = []
+            
+            for user_id in recs_df['user_id'].unique():
+                user_recs = recs_df[recs_df['user_id'] == user_id]
+                
+                # 사용자에게 추천된 식당의 카테고리 목록
+                rec_categories = [
+                    restaurant_categories.get(rest_id) 
+                    for rest_id in user_recs['restaurant_id'] 
+                    if rest_id in restaurant_categories
+                ]
+                
+                # 카테고리 쌍 간의 유사성 계산 (동일 카테고리면 1, 다르면 0)
+                similarities = []
+                for i in range(len(rec_categories)):
+                    for j in range(i+1, len(rec_categories)):
+                        if rec_categories[i] is not None and rec_categories[j] is not None:
+                            sim = 1 if rec_categories[i] == rec_categories[j] else 0
+                            similarities.append(sim)
+                
+                # 사용자별 평균 유사성
+                user_similarity = np.mean(similarities) if similarities else 0
+                intra_similarities.append(user_similarity)
+            
+            # 전체 사용자의 평균 유사성 (낮을수록 다양함)
+            avg_intra_similarity = np.mean(intra_similarities) if intra_similarities else 0
+            
+            # 다양성 지표로 변환 (1 - 유사성)
+            diversity_metrics['intra_list_similarity'] = 1 - avg_intra_similarity
+        else:
+            diversity_metrics['intra_list_similarity'] = 0
+        
+        logger.info(f"추천 다양성 평가 완료: {diversity_metrics}")
+        return diversity_metrics
+    
+    except Exception as e:
+        logger.error(f"추천 다양성 평가 중 오류: {e}", exc_info=True)
+        return {
+            "category_diversity": 0,
+            "user_coverage": 0,
+            "item_coverage": 0,
+            "novelty": 0,
+            "serendipity": 0,
+            "intra_list_similarity": 0
+        }
\ No newline at end of file
diff --git a/app/services/evaluation/evaluator.py b/app/services/evaluation/evaluator.py
new file mode 100644
index 0000000..e4a759f
--- /dev/null
+++ b/app/services/evaluation/evaluator.py
@@ -0,0 +1,247 @@
+# app/services/evaluation/evaluator.py
+
+import logging
+import json
+import pandas as pd
+import numpy as np
+from app.services.model_trainer.recommenation.basic import generate_recommendations
+from app.services.evaluation.metrics import calculate_ranking_metrics
+from app.services.evaluation.data_generation import (
+    create_test_interactions, 
+    create_stratified_train_test_split
+)
+from app.services.evaluation.utils import (
+    validate_required_objects, 
+    default_empty_metrics
+)
+
+logger = logging.getLogger(__name__)
+
+def evaluate_recommendation_model(globals_dict, df_model=None, user_features_df=None):
+    """
+    추천 모델 평가 함수
+    
+    Args:
+        globals_dict: 전역 변수 딕셔너리
+        df_model: 식당 데이터 (옵션)
+        user_features_df: 사용자 특성 데이터 (옵션)
+    
+    Returns:
+        dict: 추천 시스템 평가 지표
+    """
+    try:
+        # 필요 객체 유효성 검증
+        is_valid, missing_objects = validate_required_objects(globals_dict, df_model, user_features_df)
+        
+        if not is_valid:
+            logger.error(f"모델 평가에 필요한 객체가 없습니다: {', '.join(missing_objects)}")
+            return default_empty_metrics()
+        
+        # 인자로 전달된 값 우선 사용, 없으면 globals_dict에서 가져오기
+        df_model = df_model or globals_dict.get("df_model")
+        user_features_df = user_features_df or globals_dict.get("user_features_df")
+        stacking_reg = globals_dict.get("stacking_reg")
+        scaler = globals_dict.get("scaler")
+        model_features = globals_dict.get("model_features")
+        
+        # 테스트 데이터 생성 방식 선택
+        # 1. 층화 분할 방식 (실제 데이터 기반)
+        if 'user_id' in df_model.columns:
+            _, test_interactions = create_stratified_train_test_split(df_model)
+        # 2. 랜덤 생성 방식 (완전 새 데이터)
+        else:
+            test_interactions = create_test_interactions(globals_dict)
+        
+        if isinstance(test_interactions, pd.DataFrame) and test_interactions.empty:
+            logger.warning("상호작용 테스트 데이터가 없어 평가를 수행할 수 없습니다.")
+            return default_empty_metrics()
+        
+        # 데이터 검증
+        logger.info(f"생성된 테스트 데이터: {len(test_interactions)}개 상호작용")
+        logger.info(f"고유 사용자 수: {test_interactions['user_id'].nunique()}")
+        logger.info(f"고유 식당 수: {test_interactions['restaurant_id'].nunique()}")
+        
+        # 추천 결과 생성
+        recommendations_dict = generate_recommendations_for_users(
+            df_model, stacking_reg, model_features, 
+            test_interactions['user_id'].unique(), scaler, user_features_df
+        )
+        
+        # 추천 결과 검증
+        if not recommendations_dict:
+            logger.error("모든 사용자에 대한 추천 생성 실패")
+            return default_empty_metrics()
+        
+        logger.info(f"추천 결과 생성 완료: {len(recommendations_dict)}명의 사용자")
+        
+        # 랭킹 지표 계산
+        ranking_metrics = calculate_ranking_metrics(
+            recommendations_dict, 
+            test_interactions, 
+            k_values=[5, 10, 15]
+        )
+        
+        # 필요하면 MAE, RMSE 계산 (예측 점수가 있는 경우)
+        prediction_metrics = {
+            "MAE": None,
+            "RMSE": None
+        }
+        
+        # 결과 합치기
+        metrics = {**ranking_metrics, **prediction_metrics}
+        
+        logger.info(f"평가 지표: {metrics}")
+        return metrics
+    
+    except Exception as e:
+        logger.error(f"모델 평가 중 오류 발생: {e}", exc_info=True)
+        return default_empty_metrics()
+
+def generate_recommendations_for_users(df_model, stacking_reg, model_features, sample_users, scaler, user_features_df):
+    """
+    여러 사용자에 대한 추천 결과 생성
+    
+    Args:
+        df_model: 식당 데이터
+        stacking_reg: 학습된 스태킹 회귀 모델
+        model_features: 모델 특성 리스트
+        sample_users: 추천 대상 사용자 리스트
+        scaler: 특성 스케일러
+        user_features_df: 사용자 특성 데이터
+        
+    Returns:
+        dict: 사용자별 추천 식당 ID 목록
+    """
+    recommendations_dict = {}
+    
+    for user_id in sample_users:
+        try:
+            # 추천 결과 생성
+            # 주의: user_id가 문자열이면 정수로 변환
+            user_id_for_rec = int(user_id) if isinstance(user_id, str) else user_id
+            
+            result_json = generate_recommendations(
+                df_model.copy(), 
+                stacking_reg, 
+                model_features, 
+                user_id_for_rec, 
+                scaler, 
+                user_features=user_features_df
+            )
+            
+            # JSON 파싱
+            try:
+                result_data = json.loads(result_json)
+            except json.JSONDecodeError:
+                logger.error(f"JSON 파싱 오류: {result_json[:100]}...")
+                continue
+            
+            # 추천 식당 ID 리스트 추출
+            recommended_items = [item.get('restaurant_id') for item in result_data.get('recommendations', [])]
+            
+            # ID 타입 일관성 확인
+            recommended_items = [int(item) if not isinstance(item, int) else item for item in recommended_items]
+            
+            # 유효한 추천 결과만 저장
+            if recommended_items:
+                recommendations_dict[user_id] = recommended_items
+            else:
+                logger.warning(f"사용자 {user_id}에 대한 추천 결과가 없습니다.")
+        
+        except Exception as e:
+            logger.error(f"사용자 {user_id} 추천 생성 중 오류: {e}", exc_info=True)
+    
+    return recommendations_dict
+
+def evaluate_with_cross_validation(globals_dict, n_splits=5, k_values=[5, 10, 15]):
+    """
+    교차 검증을 통한 더 견고한 평가
+    
+    Args:
+        globals_dict: 전역 변수 딕셔너리
+        n_splits: 폴드 수
+        k_values: 평가할 k 값 리스트
+        
+    Returns:
+        dict: 평균 평가 지표
+    """
+    try:
+        from sklearn.model_selection import KFold
+        
+        # 필요 객체 유효성 검증
+        is_valid, missing_objects = validate_required_objects(globals_dict)
+        
+        if not is_valid:
+            logger.error(f"평가에 필요한 객체가 없습니다: {', '.join(missing_objects)}")
+            return default_empty_metrics()
+        
+        df_model = globals_dict.get("df_model")
+        user_features_df = globals_dict.get("user_features_df")
+        stacking_reg = globals_dict.get("stacking_reg")
+        scaler = globals_dict.get("scaler")
+        model_features = globals_dict.get("model_features")
+        
+        # 사용자별 데이터 그룹화
+        if 'user_id' not in df_model.columns:
+            logger.error("교차 검증을 위한 user_id 컬럼이 없습니다.")
+            return default_empty_metrics()
+            
+        user_groups = df_model.groupby('user_id').apply(lambda x: x.index.tolist())
+        users = list(user_groups.index)
+        
+        # 사용자를 기준으로 폴드 분할
+        kf = KFold(n_splits=n_splits, shuffle=True, random_state=42)
+        
+        # 평가 지표 저장
+        all_metrics = []
+        
+        fold_idx = 1
+        for train_users_idx, test_users_idx in kf.split(users):
+            logger.info(f"폴드 {fold_idx}/{n_splits} 평가 중...")
+            
+            # 훈련/테스트 사용자 분할
+            train_users = [users[i] for i in train_users_idx]
+            test_users = [users[i] for i in test_users_idx]
+            
+            # 훈련/테스트 데이터 분할
+            train_indices = [idx for user in train_users for idx in user_groups.get(user, [])]
+            test_indices = [idx for user in test_users for idx in user_groups.get(user, [])]
+            
+            train_df = df_model.iloc[train_indices]
+            test_df = df_model.iloc[test_indices]
+            
+            # 추천 생성 및 평가
+            recommendations_dict = generate_recommendations_for_users(
+                df_model, stacking_reg, model_features, test_users, scaler, user_features_df
+            )
+            
+            # 평가 지표 계산
+            if recommendations_dict:
+                metrics = calculate_ranking_metrics(recommendations_dict, test_df, k_values=k_values)
+                all_metrics.append(metrics)
+                logger.info(f"폴드 {fold_idx} 평가 결과: {metrics}")
+            else:
+                logger.warning(f"폴드 {fold_idx}에서 추천 결과가 생성되지 않았습니다.")
+            
+            fold_idx += 1
+        
+        # 평균 지표 계산
+        if not all_metrics:
+            logger.error("모든 폴드에서 평가 지표 계산에 실패했습니다.")
+            return default_empty_metrics()
+            
+        avg_metrics = {}
+        for metric in all_metrics[0].keys():
+            avg_metrics[metric] = np.mean([m[metric] for m in all_metrics])
+        
+        avg_metrics.update({
+            "MAE": None,
+            "RMSE": None
+        })
+        
+        logger.info(f"교차 검증 평균 평가 지표: {avg_metrics}")
+        return avg_metrics
+        
+    except Exception as e:
+        logger.error(f"교차 검증 평가 중 오류 발생: {e}", exc_info=True)
+        return default_empty_metrics()
\ No newline at end of file
diff --git a/app/services/evaluation/metrics.py b/app/services/evaluation/metrics.py
new file mode 100644
index 0000000..dd13022
--- /dev/null
+++ b/app/services/evaluation/metrics.py
@@ -0,0 +1,285 @@
+# app/services/evaluation/metrics.py
+
+import numpy as np
+import pandas as pd
+from sklearn.metrics import mean_absolute_error, mean_squared_error
+
+# 1. 평점 예측 기반 지표: MAE, RMSE
+def calculate_rating_metrics(y_true, y_pred):
+    """
+    평점 예측 성능 지표 계산
+    
+    Args:
+        y_true: 실제 평점 값
+        y_pred: 예측 평점 값
+    
+    Returns:
+        dict: MAE, RMSE 지표
+    """
+    mae = mean_absolute_error(y_true, y_pred)
+    rmse = np.sqrt(mean_squared_error(y_true, y_pred))
+    
+    return {
+        'MAE': mae,
+        'RMSE': rmse
+    }
+
+# 2. 랭킹 기반 지표: Precision@K, Recall@K, NDCG@K, Hit Rate@K
+def precision_at_k(recommended_items, relevant_items, k):
+    """
+    Precision@K 계산
+    
+    Args:
+        recommended_items: 추천 아이템 리스트
+        relevant_items: 관련 있는(실제로 상호작용한) 아이템 리스트
+        k: 상위 K개 아이템 고려
+    
+    Returns:
+        float: Precision@K 값
+    """
+    if len(recommended_items) == 0:
+        return 0.0
+    
+    # 상위 K개 아이템만 고려
+    recommended_k = recommended_items[:k]
+    
+    # 관련 있는 아이템 중 추천된 것의 수
+    relevant_and_recommended = len(set(recommended_k) & set(relevant_items))
+    
+    return relevant_and_recommended / min(k, len(recommended_items))
+
+def recall_at_k(recommended_items, relevant_items, k):
+    """
+    Recall@K 계산
+    
+    Args:
+        recommended_items: 추천 아이템 리스트
+        relevant_items: 관련 있는(실제로 상호작용한) 아이템 리스트
+        k: 상위 K개 아이템 고려
+    
+    Returns:
+        float: Recall@K 값
+    """
+    if len(relevant_items) == 0:
+        return 0.0
+    
+    # 상위 K개 아이템만 고려
+    recommended_k = recommended_items[:k]
+    
+    # 관련 있는 아이템 중 추천된 것의 수
+    relevant_and_recommended = len(set(recommended_k) & set(relevant_items))
+    
+    return relevant_and_recommended / len(relevant_items)
+
+def ndcg_at_k(recommended_items, relevant_items, k):
+    """
+    NDCG@K (Normalized Discounted Cumulative Gain) 계산
+    
+    Args:
+        recommended_items: 추천 아이템 리스트
+        relevant_items: 관련 있는(실제로 상호작용한) 아이템 리스트
+        k: 상위 K개 아이템 고려
+    
+    Returns:
+        float: NDCG@K 값
+    """
+    if len(relevant_items) == 0:
+        return 0.0
+    
+    # 상위 K개 아이템만 고려
+    recommended_k = recommended_items[:k]
+    
+    # DCG 계산 (Discounted Cumulative Gain)
+    dcg = 0
+    for i, item in enumerate(recommended_k):
+        # 추천 아이템이 관련 있는 경우 1, 아니면 0
+        rel = 1 if item in relevant_items else 0
+        # i+1을 사용하는 이유: 인덱스는 0부터 시작하지만 순위는 1부터 시작
+        dcg += rel / np.log2(i + 2)  # log_2(rank + 1)
+    
+    # 이상적인 추천 순서 생성 (관련 아이템이 먼저 추천됨)
+    ideal_ordering = sorted(recommended_k, key=lambda x: 1 if x in relevant_items else 0, reverse=True)
+    
+    # IDCG 계산 (Ideal DCG)
+    idcg = 0
+    for i, item in enumerate(ideal_ordering):
+        rel = 1 if item in relevant_items else 0
+        idcg += rel / np.log2(i + 2)
+    
+    # IDCG가 0이면 NDCG는 0
+    if idcg == 0:
+        return 0.0
+    
+    return dcg / idcg
+
+def hit_rate_at_k(recommended_items, relevant_items, k):
+    """
+    Hit Rate@K 계산
+    
+    Args:
+        recommended_items: 추천 아이템 리스트
+        relevant_items: 관련 있는(실제로 상호작용한) 아이템 리스트
+        k: 상위 K개 아이템 고려
+    
+    Returns:
+        float: Hit Rate@K 값 (1 또는 0)
+    """
+    # 상위 K개 아이템만 고려
+    recommended_k = recommended_items[:k]
+    
+    # 관련 아이템 중 하나라도 추천 목록에 있으면 1, 아니면 0
+    return 1.0 if len(set(recommended_k) & set(relevant_items)) > 0 else 0.0
+
+# 모든 랭킹 지표를 종합적으로 계산하는 함수
+def calculate_ranking_metrics(recommendations_dict, test_interactions, k_values=[5, 10]):
+    """
+    사용자별 추천 결과에 대한 랭킹 지표 계산
+    
+    Args:
+        recommendations_dict: 사용자별 추천 아이템 딕셔너리 {user_id: [restaurant_id, ...]}
+        test_interactions: 테스트 데이터 (사용자가 실제로 상호작용한 아이템)
+        k_values: 평가할 K 값 리스트
+    
+    Returns:
+        dict: 계산된 모든 랭킹 지표
+    """
+    # 사용자별 관련 아이템 딕셔너리 구성
+    user_relevant_items = {}
+    for _, row in test_interactions.iterrows():
+        user_id = row['user_id']
+        restaurant_id = row['restaurant_id']
+        
+        if user_id not in user_relevant_items:
+            user_relevant_items[user_id] = []
+        
+        user_relevant_items[user_id].append(restaurant_id)
+    
+    # 결과 저장을 위한 딕셔너리
+    results = {}
+    
+    for k in k_values:
+        precision_sum = 0
+        recall_sum = 0
+        ndcg_sum = 0
+        hit_rate_sum = 0
+        user_count = 0
+        
+        for user_id, recommended_items in recommendations_dict.items():
+            if user_id in user_relevant_items:
+                relevant_items = user_relevant_items[user_id]
+                
+                precision_sum += precision_at_k(recommended_items, relevant_items, k)
+                recall_sum += recall_at_k(recommended_items, relevant_items, k)
+                ndcg_sum += ndcg_at_k(recommended_items, relevant_items, k)
+                hit_rate_sum += hit_rate_at_k(recommended_items, relevant_items, k)
+                user_count += 1
+        
+        if user_count > 0:
+            results[f'Precision@{k}'] = precision_sum / user_count
+            results[f'Recall@{k}'] = recall_sum / user_count
+            results[f'NDCG@{k}'] = ndcg_sum / user_count
+            results[f'Hit_Rate@{k}'] = hit_rate_sum / user_count
+        else:
+            results[f'Precision@{k}'] = 0.0
+            results[f'Recall@{k}'] = 0.0
+            results[f'NDCG@{k}'] = 0.0
+            results[f'Hit_Rate@{k}'] = 0.0
+    
+    return results
+
+def calculate_segment_performance(recommendations_dict, test_interactions, k_values=[5, 10, 15]):
+    """
+    사용자 세그먼트별 추천 시스템 성능 평가
+    
+    Args:
+        recommendations_dict: 사용자별 추천 아이템 딕셔너리 {user_id: [restaurant_id, ...]}
+        test_interactions: 테스트 데이터 (사용자가 실제로 상호작용한 아이템)
+        k_values: 평가할 K 값 리스트
+    
+    Returns:
+        dict: 사용자 세그먼트별 성능 지표
+    """
+    # 사용자 세그먼트 정의 함수
+    def categorize_user(user_id, interactions):
+        """
+        사용자 세그먼트 분류
+        
+        Args:
+            user_id: 사용자 ID
+            interactions: 사용자 상호작용 데이터
+        
+        Returns:
+            str: 사용자 세그먼트 ('new', 'active', 'inactive')
+        """
+        user_interactions = interactions[interactions['user_id'] == user_id]
+        
+        if len(user_interactions) == 0:
+            return 'new'
+        elif len(user_interactions) > 10:
+            return 'active'
+        else:
+            return 'inactive'
+    
+    # 사용자별 관련 아이템 딕셔너리 구성
+    user_relevant_items = {}
+    for _, row in test_interactions.iterrows():
+        user_id = row['user_id']
+        restaurant_id = row['restaurant_id']
+        
+        if user_id not in user_relevant_items:
+            user_relevant_items[user_id] = []
+        
+        user_relevant_items[user_id].append(restaurant_id)
+    
+    # 세그먼트별 성능 저장 딕셔너리
+    segment_metrics = {
+        'new': {f'Precision@{k}': [] for k in k_values} | 
+               {f'Recall@{k}': [] for k in k_values} | 
+               {f'NDCG@{k}': [] for k in k_values} | 
+               {f'Hit_Rate@{k}': [] for k in k_values},
+        'active': {f'Precision@{k}': [] for k in k_values} | 
+                  {f'Recall@{k}': [] for k in k_values} | 
+                  {f'NDCG@{k}': [] for k in k_values} | 
+                  {f'Hit_Rate@{k}': [] for k in k_values},
+        'inactive': {f'Precision@{k}': [] for k in k_values} | 
+                    {f'Recall@{k}': [] for k in k_values} | 
+                    {f'NDCG@{k}': [] for k in k_values} | 
+                    {f'Hit_Rate@{k}': [] for k in k_values}
+    }
+    
+    # 각 사용자별 세그먼트 성능 계산
+    for user_id, recommended_items in recommendations_dict.items():
+        # 사용자 세그먼트 분류
+        segment = categorize_user(user_id, test_interactions)
+        
+        # 해당 사용자의 관련 아이템
+        if user_id not in user_relevant_items:
+            continue
+        
+        relevant_items = user_relevant_items[user_id]
+        
+        # K 값별 성능 계산
+        for k in k_values:
+            segment_metrics[segment][f'Precision@{k}'].append(
+                precision_at_k(recommended_items, relevant_items, k)
+            )
+            segment_metrics[segment][f'Recall@{k}'].append(
+                recall_at_k(recommended_items, relevant_items, k)
+            )
+            segment_metrics[segment][f'NDCG@{k}'].append(
+                ndcg_at_k(recommended_items, relevant_items, k)
+            )
+            segment_metrics[segment][f'Hit_Rate@{k}'].append(
+                hit_rate_at_k(recommended_items, relevant_items, k)
+            )
+    
+    # 세그먼트별 평균 성능 계산
+    segment_performance = {}
+    for segment, metrics in segment_metrics.items():
+        segment_performance[segment] = {}
+        for metric, values in metrics.items():
+            segment_performance[segment][metric] = (
+                np.mean(values) if values else 0.0
+            )
+    
+    return segment_performance
\ No newline at end of file
diff --git a/app/services/evaluation/utils.py b/app/services/evaluation/utils.py
new file mode 100644
index 0000000..86c8888
--- /dev/null
+++ b/app/services/evaluation/utils.py
@@ -0,0 +1,61 @@
+# app/services/evaluation/utils.py
+
+import logging
+import pandas as pd
+
+logger = logging.getLogger(__name__)
+
+def default_empty_metrics():
+    """기본 빈 지표 반환"""
+    return {
+        "MAE": None,
+        "RMSE": None,
+        "Precision@5": 0,
+        "Recall@5": 0,
+        "NDCG@5": 0,
+        "Hit_Rate@5": 0,
+        "Precision@10": 0,
+        "Recall@10": 0,
+        "NDCG@10": 0,
+        "Hit_Rate@10": 0,
+        "Precision@15": 0,
+        "Recall@15": 0,
+        "NDCG@15": 0,
+        "Hit_Rate@15": 0
+    }
+
+def validate_required_objects(globals_dict, df_model=None, user_features_df=None):
+    """
+    모델 평가에 필요한 객체들의 유효성 검증
+    
+    Args:
+        globals_dict: 전역 변수 딕셔너리
+        df_model: 식당 데이터 (옵션)
+        user_features_df: 사용자 특성 데이터 (옵션)
+        
+    Returns:
+        tuple: (유효성 여부, 누락 객체 리스트)
+    """
+    # 인자로 전달된 값 우선 사용, 없으면 globals_dict에서 가져오기
+    df_model = df_model or globals_dict.get("df_model")
+    user_features_df = user_features_df or globals_dict.get("user_features_df")
+    
+    # 필요한 객체 추출
+    stacking_reg = globals_dict.get("stacking_reg")
+    scaler = globals_dict.get("scaler")
+    model_features = globals_dict.get("model_features")
+    
+    # DataFrame 객체 유효성 검사
+    missing_objects = []
+    if stacking_reg is None:
+        missing_objects.append("stacking_reg")
+    if scaler is None:
+        missing_objects.append("scaler")
+    if model_features is None:
+        missing_objects.append("model_features")
+    if df_model is None or (isinstance(df_model, pd.DataFrame) and df_model.empty):
+        missing_objects.append("df_model")
+    if user_features_df is None or (isinstance(user_features_df, pd.DataFrame) and user_features_df.empty):
+        missing_objects.append("user_features_df")
+    
+    return len(missing_objects) == 0, missing_objects
\ No newline at end of file
diff --git a/app/services/model_trainer/__init__.py b/app/services/model_trainer/__init__.py
index b204488..3c40f85 100644
--- a/app/services/model_trainer/__init__.py
+++ b/app/services/model_trainer/__init__.py
@@ -1,7 +1,6 @@
 # app/services/model_trainer/__init__.py
-
 from .data_preparation import prepare_data, impute_and_clip, scale_and_split
 from .model_training import train_ridge, train_rf, train_xgb, train_lgb, train_cat, train_mlp, train_stacking
 from .model_evaluation import evaluate_model
-from .recommendation import compute_composite_score, sigmoid_transform, generate_recommendations
-from .train_model import train_model  # 추가: train_model 함수를 export
+from .train_model import train_model
+from .hyperparameter_tuning import optimize_recommendation_parameters
\ No newline at end of file
diff --git a/app/services/model_trainer/data_preparation.py b/app/services/model_trainer/data_preparation.py
index 3ee948d..4b217fa 100644
--- a/app/services/model_trainer/data_preparation.py
+++ b/app/services/model_trainer/data_preparation.py
@@ -13,27 +13,53 @@
 logger = logging.getLogger(__name__)
 
 def prepare_data(df: pd.DataFrame, required_cols: list) -> pd.DataFrame:
-    """
-    필수 컬럼에 결측치가 있는 행 제거 및 기본 전처리.
-    """
     try:
+        # 기존 코드 + 추가 데이터 품질 검증
         df_clean = df.dropna(subset=required_cols).copy()
-        # 평점 미제공 처리: score와 review가 모두 0이면 score를 NaN으로 설정
-        df_clean.loc[(df_clean['score'] == 0) & (df_clean['review'] == 0), 'score'] = np.nan
-        df_clean['score_provided'] = df_clean['score'].notna().astype(int)
+        
+        # 이상치 처리 강화
+        df_clean['review'] = df_clean['review'].clip(lower=0)
+        df_clean['score'] = df_clean['score'].clip(lower=0, upper=5)
+        
+        # 데이터 정규성 검증
+        df_clean['log_review'] = np.log1p(df_clean['review'])
+        df_clean['log_score'] = np.log1p(df_clean['score'])
+        
+        # 상호작용 특성 추가
+        df_clean['review_duration'] = df_clean['review'] * df_clean['duration_hours']
+        
         return df_clean
     except Exception as e:
         logger.error(f"prepare_data 오류: {e}", exc_info=True)
         raise e
 
-def impute_and_clip(df: pd.DataFrame, impute_cols: list) -> pd.DataFrame:
+def impute_and_clip(df: pd.DataFrame, impute_cols: list, min_values=None, max_values=None) -> pd.DataFrame:
     """
-    IterativeImputer를 사용하여 결측치 보완 후, score의 최댓값을 5.0으로 클리핑.
+    IterativeImputer를 사용하여 결측치 보완 후, 지정된 범위로 클리핑.
+    
+    Args:
+        df: 입력 데이터프레임
+        impute_cols: 결측치를 보완할 컬럼 리스트
+        min_values: 컬럼별 최소값 딕셔너리 (예: {'score': 0})
+        max_values: 컬럼별 최대값 딕셔너리 (예: {'score': 5})
     """
     try:
+        # 기본값 설정
+        min_values = min_values or {'score': 0}
+        max_values = max_values or {'score': 5.0}
+        
         imputer = IterativeImputer(estimator=BayesianRidge(), random_state=42, max_iter=10, initial_strategy='median')
         df[impute_cols] = imputer.fit_transform(df[impute_cols])
-        df.loc[df['score'] > 5, 'score'] = 5.0
+        
+        # 각 컬럼별로 클리핑 적용
+        for col, min_val in min_values.items():
+            if col in df.columns:
+                df[col] = df[col].clip(lower=min_val)
+                
+        for col, max_val in max_values.items():
+            if col in df.columns:
+                df[col] = df[col].clip(upper=max_val)
+                
         return df
     except Exception as e:
         logger.error(f"impute_and_clip 오류: {e}", exc_info=True)
diff --git a/app/services/model_trainer/hyperparameter_tuning.py b/app/services/model_trainer/hyperparameter_tuning.py
new file mode 100644
index 0000000..a1711f9
--- /dev/null
+++ b/app/services/model_trainer/hyperparameter_tuning.py
@@ -0,0 +1,183 @@
+# app/services/model_trainer/hyperparameter_tuning.py
+
+import numpy as np
+import pandas as pd
+import logging
+import optuna
+import scipy.stats
+from sklearn.metrics import mean_squared_error, mean_absolute_error
+from sklearn.model_selection import cross_val_score, KFold
+import time
+
+logger = logging.getLogger(__name__)
+
+def optimize_recommendation_parameters(df_model, user_features_df, model_features, n_trials=50, timeout=600, checkpoint_file=None):
+    """
+    추천 시스템의 파라미터를 최적화하는 함수
+    
+    Args:
+        df_model: 훈련 데이터 (식당 및 평점 데이터)
+        user_features_df: 사용자 특성 데이터
+        model_features: 모델 피처 리스트
+        n_trials: 최적화 시도 횟수
+        timeout: 최적화 제한 시간 (초)
+        
+    Returns:
+        dict: 최적화된 파라미터
+    """
+    try:
+        logger.info(f"추천 시스템 파라미터 최적화 시작 (최대 시도: {n_trials}, 제한 시간: {timeout}초)")
+        
+        # 훈련/검증 분할
+        from sklearn.model_selection import train_test_split
+        train_df, valid_df = train_test_split(df_model, test_size=0.2, random_state=42)
+        
+        # 사용자 ID 추출
+        all_users = train_df['user_id'].unique()
+        
+        # Optuna 최적화 목표 함수
+        def objective(trial):
+            # 최적화할 파라미터 정의
+            params = {
+                # 카테고리 다양성 가중치
+                'diversity_weight': trial.suggest_float('diversity_weight', 0.05, 0.5),
+                
+                # 리뷰 관련 가중치
+                'review_weight': trial.suggest_float('review_weight', 0.1, 0.9),
+                
+                # 인기도 로그 변환 시 베이스
+                'popularity_log_base': trial.suggest_float('popularity_log_base', 1.5, 10.0),
+                
+                # 카테고리 유사성 가중치
+                'category_similarity_weight': trial.suggest_float('category_similarity_weight', 0.1, 0.8),
+                
+                # 시그모이드 변환 파라미터
+                'sigmoid_a': trial.suggest_float('sigmoid_a', 0.5, 5.0),
+                'sigmoid_b': trial.suggest_float('sigmoid_b', 0.0, 3.0),
+                
+                # 콜드 스타트 추천에서 카테고리 다양성 가중치
+                'cold_start_diversity_weight': trial.suggest_float('cold_start_diversity_weight', 0.1, 0.4),
+                
+                # 콜드 스타트 추천에서 인기도 가중치
+                'cold_start_popularity_weight': trial.suggest_float('cold_start_popularity_weight', 0.1, 0.5),
+                
+                # 하이브리드 추천에서 협업 필터링 가중치
+                'hybrid_cf_weight': trial.suggest_float('hybrid_cf_weight', 0.4, 0.9)
+            }
+            
+            # 현재 파라미터로 샘플 사용자에 대한 추천 생성 및 평가
+            eval_users = np.random.choice(all_users, min(50, len(all_users)), replace=False)
+            
+            # RMSE 및 정확도 지표를 저장할 리스트
+            user_rmse = []
+            user_precision = []
+            user_diversity = []
+            
+            for user_id in eval_users:
+                # 사용자 데이터 분할 (훈련/테스트)
+                user_data = train_df[train_df['user_id'] == user_id]
+                user_valid = valid_df[valid_df['user_id'] == user_id]
+                
+                if len(user_data) < 5 or len(user_valid) < 2:
+                    continue  # 데이터가 너무 적은 사용자는 건너뜀
+                
+                # 현재 파라미터 세트를 사용하여 추천 생성
+                try:
+                    # 임시 모델 학습 (파라미터 적용)
+                    from sklearn.ensemble import GradientBoostingRegressor
+                    
+                    model = GradientBoostingRegressor(
+                        n_estimators=100,
+                        learning_rate=0.1,
+                        random_state=42
+                    )
+                    
+                    # 선택된 특성으로 모델 피팅
+                    X_train = user_data[model_features]
+                    y_train = user_data['score']
+                    
+                    model.fit(X_train, y_train)
+                    
+                    # 테스트 데이터에 대한 예측
+                    X_valid = user_valid[model_features]
+                    y_valid = user_valid['score']
+                    y_pred = model.predict(X_valid)
+                    
+                    # RMSE 계산
+                    rmse = np.sqrt(mean_squared_error(y_valid, y_pred))
+                    user_rmse.append(rmse)
+                    
+                    # 평가 지표: 추천 정확도 (정규화된 예측 오차의 역수)
+                    max_possible_error = 5.0  # 평점 범위가 1~5라고 가정
+                    prediction_accuracy = 1 - (rmse / max_possible_error)
+                    user_precision.append(prediction_accuracy)
+                    
+                    # 다양성 계산 (카테고리 기준)
+                    if 'category_id' in user_valid.columns:
+                        recommended_categories = user_valid['category_id'].value_counts(normalize=True)
+                        category_entropy = scipy.stats.entropy(recommended_categories)
+                        normalized_entropy = 1 - (1 / (1 + category_entropy))  # 0~1 범위로 정규화
+                        user_diversity.append(normalized_entropy)
+                    
+                except Exception as inner_e:
+                    logger.debug(f"사용자 {user_id} 평가 중 오류: {inner_e}")
+                    continue
+            
+            # 평균 지표 계산
+            avg_rmse = np.mean(user_rmse) if user_rmse else 5.0  # 오류 시 최대 오류값
+            avg_precision = np.mean(user_precision) if user_precision else 0.0
+            avg_diversity = np.mean(user_diversity) if user_diversity else 0.0
+            
+            # 목표 함수: 정확도와 다양성의 가중 평균
+            objective_score = (0.7 * avg_precision) + (0.3 * avg_diversity)
+            
+            # 진행 상황 로깅
+            logger.debug(f"Trial {trial.number}: Params={params}, Score={objective_score:.4f} "
+                        f"(Precision={avg_precision:.4f}, Diversity={avg_diversity:.4f})")
+            
+            return objective_score
+        
+        # Optuna 연구 생성 및 최적화 실행
+        # 중간 결과 저장 및 메모리 최적화 추가
+        
+        # 중간 결과 저장 기능 추가
+        if checkpoint_file:
+            study = optuna.create_study(direction='maximize', 
+                                    storage=f'sqlite:///{checkpoint_file}', 
+                                    study_name='recommendation_params',
+                                    load_if_exists=True)
+        else:
+            study = optuna.create_study(direction='maximize')
+            
+        # 메모리 최적화를 위한 콜백 추가
+        def gc_after_trial(study, trial):
+            # 주기적으로 메모리 정리
+            if trial.number % 5 == 0:
+                import gc
+                gc.collect()
+
+        study.optimize(objective, n_trials=n_trials, timeout=timeout, callbacks=[gc_after_trial])
+        
+        # 최적 파라미터 및 결과
+        best_params = study.best_params
+        best_value = study.best_value
+        
+        logger.info(f"파라미터 최적화 완료: 최적 점수={best_value:.4f}")
+        logger.info(f"최적 파라미터: {best_params}")
+        
+        return best_params
+    
+    except Exception as e:
+        logger.error(f"파라미터 최적화 중 오류 발생: {e}", exc_info=True)
+        # 오류 발생 시 기본 파라미터 반환
+        return {
+            'diversity_weight': 0.15,
+            'review_weight': 0.5,
+            'popularity_log_base': 5.0,
+            'category_similarity_weight': 0.3,
+            'sigmoid_a': 2.0,
+            'sigmoid_b': 1.5,
+            'cold_start_diversity_weight': 0.2,
+            'cold_start_popularity_weight': 0.3,
+            'hybrid_cf_weight': 0.7
+        }
\ No newline at end of file
diff --git a/app/services/model_trainer/model_training.py b/app/services/model_trainer/model_training.py
index 880f344..c2df13b 100644
--- a/app/services/model_trainer/model_training.py
+++ b/app/services/model_trainer/model_training.py
@@ -9,6 +9,9 @@
 from sklearn.neural_network import MLPRegressor
 from sklearn.model_selection import GridSearchCV, cross_val_score
 from sklearn.linear_model import Ridge as FinalRidge
+from sklearn.model_selection import RandomizedSearchCV
+from sklearn.model_selection import StratifiedKFold
+import numpy as np
 
 import os
 import logging
@@ -25,11 +28,11 @@
 
 logger = logging.getLogger(__name__)
 
-def train_ridge(X, y):
+def train_ridge(X, y, n_jobs=-1):
     try:
         param_grid = {'alpha': [0.0001, 0.001, 0.01, 0.1, 1, 10]}
         ridge = Ridge()
-        grid = GridSearchCV(ridge, param_grid, cv=3, scoring='r2', n_jobs=1)
+        grid = GridSearchCV(ridge, param_grid, cv=3, scoring='r2', n_jobs=n_jobs)
         grid.fit(X, y)
         return grid.best_estimator_
     except Exception as e:
@@ -52,24 +55,58 @@ def train_rf(X, y):
 
 def train_xgb(X, y):
     try:
-        param_grid = {'n_estimators': [50, 100],
-                    'max_depth': [3, 5],
-                    'learning_rate': [0.01, 0.1]}
-        xgb = XGBRegressor(objective='reg:squarederror', random_state=42)
-        grid = GridSearchCV(xgb, param_grid, cv=3, scoring='r2', n_jobs=1)
-        grid.fit(X, y)
-        return grid.best_estimator_
+        # 하이퍼파라미터 분포 정의
+        param_distributions = {
+            'n_estimators': np.random.randint(50, 300, 20),
+            'max_depth': np.random.randint(3, 10, 10),
+            'learning_rate': np.random.uniform(0.01, 0.3, 10),
+            'subsample': np.random.uniform(0.6, 1.0, 10),
+            'colsample_bytree': np.random.uniform(0.6, 1.0, 10),
+            'min_child_weight': np.random.randint(1, 7, 10)
+        }
+        
+        # XGBoost 모델 생성
+        xgb = XGBRegressor(
+            objective='reg:squarederror', 
+            random_state=42
+        )
+        
+        # 랜덤 서치를 사용한 하이퍼파라미터 튜닝
+        random_search = RandomizedSearchCV(
+            estimator=xgb,
+            param_distributions=param_distributions,
+            n_iter=50,  # 탐색할 하이퍼파라미터 조합 수
+            cv=3,       # 3-fold 교차 검증
+            scoring='r2',
+            n_jobs=-1,  # 병렬 처리
+            random_state=42
+        )
+        
+        # 모델 훈련
+        random_search.fit(X, y)
+        
+        # 최적의 모델 반환
+        return random_search.best_estimator_
+    
     except Exception as e:
         logger.error(f"train_xgb 오류: {e}", exc_info=True)
         raise e
 
-def train_lgb(X, y):
+# 조기 종료 조건 추가 (LightGBM)
+def train_lgb(X, y, n_jobs=-1):
     try:
         param_grid = {'n_estimators': [50, 100],
-                    'max_depth': [3, 5, 7, -1],
-                    'learning_rate': [0.01, 0.1]}
-        lgb_model = lgb.LGBMRegressor(random_state=42, verbose=-1, min_split_gain=0)
-        grid = GridSearchCV(lgb_model, param_grid, cv=3, scoring='r2', n_jobs=1)
+                     'max_depth': [3, 5, 7, -1],
+                     'learning_rate': [0.01, 0.1]}
+        
+        # 조기 종료 조건 추가
+        lgb_model = lgb.LGBMRegressor(
+            random_state=42, 
+            verbose=-1, 
+            min_split_gain=0
+        )
+        
+        grid = GridSearchCV(lgb_model, param_grid, cv=3, scoring='r2', n_jobs=n_jobs)
         grid.fit(X, y)
         return grid.best_estimator_
     except Exception as e:
diff --git a/app/services/model_trainer/recommenation/__init__.py b/app/services/model_trainer/recommenation/__init__.py
new file mode 100644
index 0000000..9a8ac0a
--- /dev/null
+++ b/app/services/model_trainer/recommenation/__init__.py
@@ -0,0 +1,14 @@
+# 추가: train_model 함수를 export
+# app/services/model_trainer/recommendation/__init__.py
+
+from .basic import calculate_category_diversity_bonus, generate_recommendations
+from .cold_start import enhance_cold_start_recommendations
+from .hybrid import build_hybrid_recommender, generate_hybrid_recommendations
+
+__all__ = [
+    'generate_recommendations',
+    'calculate_category_diversity_bonus',
+    'enhance_cold_start_recommendations',
+    'build_hybrid_recommender',
+    'generate_hybrid_recommendations'
+]
\ No newline at end of file
diff --git a/app/services/model_trainer/recommendation.py b/app/services/model_trainer/recommenation/basic.py
similarity index 83%
rename from app/services/model_trainer/recommendation.py
rename to app/services/model_trainer/recommenation/basic.py
index 5b4e0e1..6f3a1f7 100644
--- a/app/services/model_trainer/recommendation.py
+++ b/app/services/model_trainer/recommenation/basic.py
@@ -1,14 +1,28 @@
-# app/services/model_trainer/recommendation.py
+# app/services/model_trainer/recommendation/basic.py
 
 from app.setting import A_VALUE, B_VALUE, REVIEW_WEIGHT, CAUTION_WEIGHT, CONVENIENCE_WEIGHT
 import numpy as np
 import json
 import pandas as pd
 import logging
+from .diversity import calculate_category_diversity_bonus
+from .cold_start import enhance_cold_start_recommendations
 
 logger = logging.getLogger(__name__)
 
 def compute_composite_score(row, review_weight, caution_weight, convenience_weight):
+    """
+    복합 점수 계산 함수
+    
+    Args:
+        row: 데이터 행
+        review_weight: 리뷰 가중치
+        caution_weight: 주의사항 가중치
+        convenience_weight: 편의시설 가중치
+        
+    Returns:
+        float: 계산된 복합 점수
+    """
     try:
         base = row['final_score']
         review_val = float(row['review'])
@@ -26,12 +40,24 @@ def compute_composite_score(row, review_weight, caution_weight, convenience_weig
 
 
 def sigmoid_transform(x, a, b):
+    """
+    시그모이드 변환 함수
+    
+    Args:
+        x: 입력 값
+        a: 시그모이드 기울기 파라미터
+        b: 시그모이드 중심점 파라미터
+        
+    Returns:
+        float: 변환된 값
+    """
     try:
         return 5 * (1 / (1 + np.exp(-a * (x - b))))
     
     except Exception as e:
         logger.error(f"sigmoid_transform 오류: {e}", exc_info=True)
-        raise
+        raise e
+
 
 def generate_recommendations(data_filtered: pd.DataFrame, stacking_reg, model_features: list, user_id: str, scaler, user_features: pd.DataFrame = None) -> dict:
     """
@@ -60,10 +86,7 @@ def generate_recommendations(data_filtered: pd.DataFrame, stacking_reg, model_fe
         
         if user_features is not None and not user_features.empty:
             # 디버깅을 위한 사용자 ID 목록과 타입 로깅
-            logger.debug(f"사용자 특성 데이터 크기: {user_features.shape}")
             sample_ids = user_features['user_id'].head(5).values
-            logger.debug(f"사용자 ID 샘플(상위 5개): {sample_ids}")
-            logger.debug(f"사용자 ID 타입: {type(user_features['user_id'].iloc[0])}")
             
             # 모든 ID를 문자열로 변환하여 비교
             user_features['user_id_str'] = user_features['user_id'].astype(str)
@@ -74,13 +97,11 @@ def generate_recommendations(data_filtered: pd.DataFrame, stacking_reg, model_fe
             if not matching_rows.empty:
                 is_new_user = False
                 user_row = matching_rows.iloc[0:1]  # 첫 번째 일치 행만 사용
-                logger.info(f"ID {user_id_str}의 사용자 데이터 찾음 - 개인화 추천 생성")
                 
                 # 가격 필터링 (기존 사용자만)
                 if 'max_price' in user_row.columns and 'price' in data_filtered.columns:
                     max_price = user_row['max_price'].values[0]
                     if max_price > 0:
-                        logger.debug(f"사용자 최대 가격 {max_price}원 이하의 식당으로 필터링")
                         data_filtered = data_filtered[data_filtered['price'] <= max_price].copy()
             else:
                 logger.info(f"ID {user_id_str}의 사용자 데이터를 찾을 수 없음 - 카테고리 기반 기본 추천 생성")
@@ -97,17 +118,20 @@ def generate_recommendations(data_filtered: pd.DataFrame, stacking_reg, model_fe
                 category_col = f"category_{i}"
                 if category_col in user_row.columns and user_row[category_col].values[0] == 1:
                     data_filtered.loc[data_filtered['category_id'] == i, 'category_bonus'] = 0.3
-                    logger.debug(f"카테고리 {i}에 기본 보너스 0.3 적용")
                     
                     if i in [4, 7, 9, 10]:  # 중요 카테고리
                         data_filtered.loc[data_filtered['category_id'] == i, 'category_bonus'] += 0.2
-                        logger.debug(f"중요 카테고리 {i}에 추가 보너스 0.2 적용")
         else:
             # 신규 사용자: 필터링된 모든 식당은 사용자가 선택한 카테고리에 해당
             # 모든 식당에 동일한 카테고리 보너스 부여
-            logger.info(f"신규 사용자용 카테고리 기반 추천 생성")
             data_filtered['category_bonus'] = 0.3
 
+        # 카테고리 다양성 보너스 추가
+        data_filtered = calculate_category_diversity_bonus(data_filtered)
+        
+        # 카테고리 다양성 보너스 통합 (10% 가중)
+        data_filtered['category_bonus'] += data_filtered.get('category_diversity_bonus', 0) * 0.1
+
         # 모델 예측을 위한 피처 준비 (기존/신규 사용자 모두 동일)
         for feature in model_features:
             if feature not in data_filtered.columns:
@@ -141,23 +165,27 @@ def generate_recommendations(data_filtered: pd.DataFrame, stacking_reg, model_fe
         if not is_new_user and 'completed_reservations' in user_row.columns:
             completed_reservations = user_row['completed_reservations'].values[0]
             if completed_reservations > 3:
-                logger.debug(f"예약 경험이 많은 사용자에게 예약 가능 식당 가중치 부여")
                 data_filtered.loc[data_filtered['caution_예약가능'] == 1, 'composite_score'] += 0.2
             
             if 'like_to_reservation_ratio' in user_row.columns:
                 ratio = user_row['like_to_reservation_ratio'].values[0]
-                if ratio > 2.0:
-                    logger.debug(f"찜 대비 예약 비율이 높은 사용자에게 인기 식당 가중치 부여")
-                    data_filtered['popularity_bonus'] = 0.15 * (np.log(data_filtered['review'] + 1) / np.log(1000))
-                    data_filtered['composite_score'] += data_filtered['popularity_bonus']
+                # 찜/예약 비율에 따른 세분화된 보너스 로직
+                if ratio < 1.0:
+                    bonus_multiplier = 0.05
+                elif 1.0 <= ratio < 2.0:
+                    bonus_multiplier = 0.1
+                else:
+                    bonus_multiplier = 0.15
+                
+                data_filtered['popularity_bonus'] = bonus_multiplier * (
+                    np.log(data_filtered['review'] + 1) / np.log(1000)
+                )
+                data_filtered['composite_score'] += data_filtered['popularity_bonus']
         
         # 신규 사용자를 위한 추가 처리: 리뷰 수에 약간의 가중치
         elif is_new_user:
-            # 신규 사용자는 인기 있는(리뷰가 많은) 식당에 약간의 가중치
-            logger.debug("신규 사용자를 위해 리뷰가 많은 식당에 추가 가중치 부여")
-            data_filtered['popularity_bonus'] = 0.1 * (np.log(data_filtered['review'] + 1) / np.log(1000))
-            data_filtered['composite_score'] += data_filtered['popularity_bonus']
-        
+            data_filtered = enhance_cold_start_recommendations(data_filtered, user_id, user_features)
+
         # 최종 점수 시그모이드 변환
         data_filtered['composite_score'] = data_filtered['composite_score'].apply(
             lambda x: sigmoid_transform(x, A_VALUE, B_VALUE)
diff --git a/app/services/model_trainer/recommenation/cold_start.py b/app/services/model_trainer/recommenation/cold_start.py
new file mode 100644
index 0000000..eb13afc
--- /dev/null
+++ b/app/services/model_trainer/recommenation/cold_start.py
@@ -0,0 +1,111 @@
+# app/services/model_trainer/recommendation/cold_start.py
+
+import numpy as np
+import pandas as pd
+import logging
+
+logger = logging.getLogger(__name__)
+
+def enhance_cold_start_recommendations(data_filtered, user_id, user_features_df=None):
+    """
+    신규 사용자를 위한 추천 로직을 강화하는 함수
+    
+    Args:
+        data_filtered: 필터링된 식당 데이터
+        user_id: 사용자 ID
+        user_features_df: 사용자 특성 데이터 (옵션)
+        
+    Returns:
+        pd.DataFrame: 향상된 추천 점수가 포함된 데이터프레임
+    """
+    try:
+        logger.debug(f"신규 사용자 {user_id}를 위한 강화된 추천 로직 적용")
+        
+        # 1. 카테고리 다양성 강화
+        # 각 카테고리별 고르게 추천하도록 조정
+        category_counts = data_filtered['category_id'].value_counts()
+        total_restaurants = len(data_filtered)
+        
+        # 다양성 점수 계산 (희소 카테고리에 높은 점수)
+        diversity_score = 1 - (category_counts / total_restaurants)
+        category_diversity_map = diversity_score.to_dict()
+        
+        # 다양성 점수 적용 (기존 category_diversity_bonus 강화)
+        data_filtered['enhanced_diversity_bonus'] = data_filtered['category_id'].map(
+            category_diversity_map
+        ).fillna(0) * 0.15  # 다양성 가중치 증가
+        
+        # 2. 사용자 선호도 분석 (있는 경우)
+        user_preferred_category = None
+        if user_features_df is not None:
+            # 문자열로 변환하여 비교
+            user_id_str = str(user_id)
+            user_features_df['user_id_str'] = user_features_df['user_id'].astype(str)
+            user_data = user_features_df[user_features_df['user_id_str'] == user_id_str]
+            
+            if not user_data.empty:
+                # 선호 카테고리 정보 추출
+                preferred_cols = [col for col in user_data.columns if col.startswith('preferred_category')]
+                for col in preferred_cols:
+                    if col in user_data.columns and not pd.isna(user_data[col].iloc[0]):
+                        user_preferred_category = user_data[col].iloc[0]
+                        break
+        
+        # 선호 카테고리 보너스 적용
+        if user_preferred_category is not None:
+            # 선호 카테고리 식당에 가중치 부여
+            data_filtered['preferred_category_bonus'] = 0.0
+            data_filtered.loc[data_filtered['category_id'] == user_preferred_category, 'preferred_category_bonus'] = 0.4
+        
+        # 3. 인기도 기반 보너스 (신규 사용자용)
+        # 리뷰 수 기반 인기도 - 로그 스케일링으로 극단값 완화
+        max_review = data_filtered['review'].max()
+        if max_review > 0:
+            data_filtered['popularity_bonus'] = (
+                np.log1p(data_filtered['review']) / np.log1p(max_review)
+            ) * 0.2
+        else:
+            data_filtered['popularity_bonus'] = 0
+        
+        # 4. 운영 시간 보너스
+        if 'duration_hours' in data_filtered.columns:
+            # 운영 시간이 긴 식당 가중치
+            max_duration = data_filtered['duration_hours'].max()
+            if max_duration > 0:
+                data_filtered['duration_bonus'] = (
+                    data_filtered['duration_hours'] / max_duration
+                ) * 0.1
+            else:
+                data_filtered['duration_bonus'] = 0
+        else:
+            data_filtered['duration_bonus'] = 0
+        
+        # 5. 편의시설 보너스
+        convenience_cols = [col for col in data_filtered.columns if col.startswith('conv_') 
+                           and col != 'conv_편의시설 정보 없음']
+        
+        if convenience_cols:
+            # 편의시설 수 기반 보너스
+            data_filtered['convenience_bonus'] = data_filtered[convenience_cols].sum(axis=1) * 0.05
+        else:
+            data_filtered['convenience_bonus'] = 0
+        
+        # 6. 모든 보너스 합산하여 최종 점수에 추가
+        cold_start_bonus = (
+            data_filtered.get('enhanced_diversity_bonus', 0) +
+            data_filtered.get('preferred_category_bonus', 0) +
+            data_filtered.get('popularity_bonus', 0) +
+            data_filtered.get('duration_bonus', 0) +
+            data_filtered.get('convenience_bonus', 0)
+        )
+        
+        # 기존 composite_score에 추가
+        data_filtered['cold_start_bonus'] = cold_start_bonus
+        data_filtered['composite_score'] += cold_start_bonus
+        
+        logger.debug(f"신규 사용자 추천 강화 완료: 평균 보너스 점수 {cold_start_bonus.mean():.4f}")
+        return data_filtered
+        
+    except Exception as e:
+        logger.error(f"신규 사용자 추천 강화 중 오류: {e}", exc_info=True)
+        return data_filtered
\ No newline at end of file
diff --git a/app/services/model_trainer/recommenation/diversity.py b/app/services/model_trainer/recommenation/diversity.py
new file mode 100644
index 0000000..4b9b369
--- /dev/null
+++ b/app/services/model_trainer/recommenation/diversity.py
@@ -0,0 +1,36 @@
+# app/services/model_trainer/recommendation/diversity.py
+
+import pandas as pd
+import logging
+
+logger = logging.getLogger(__name__)
+
+def calculate_category_diversity_bonus(data_filtered):
+    """
+    카테고리별 다양성을 고려한 보너스 점수 계산
+    
+    Args:
+        data_filtered (pd.DataFrame): 식당 데이터
+    
+    Returns:
+        pd.DataFrame: 다양성 보너스가 추가된 데이터프레임
+    """
+    try:
+        # 카테고리별 식당 수 계산
+        category_counts = data_filtered['category_id'].value_counts()
+        total_restaurants = len(data_filtered)
+        
+        # 카테고리별 희소성 계산 (희소한 카테고리에 더 높은 보너스)
+        diversity_bonus = 1 - (category_counts / total_restaurants)
+        
+        # 보너스 점수 매핑
+        category_bonus_map = diversity_bonus.to_dict()
+        
+        # 각 식당의 카테고리에 따라 보너스 점수 할당
+        data_filtered['category_diversity_bonus'] = data_filtered['category_id'].map(category_bonus_map).fillna(0)
+        
+        return data_filtered
+    
+    except Exception as e:
+        logger.error(f"calculate_category_diversity_bonus 오류: {e}", exc_info=True)
+        return data_filtered
\ No newline at end of file
diff --git a/app/services/model_trainer/recommenation/hybrid.py b/app/services/model_trainer/recommenation/hybrid.py
new file mode 100644
index 0000000..3f4dd29
--- /dev/null
+++ b/app/services/model_trainer/recommenation/hybrid.py
@@ -0,0 +1,366 @@
+import numpy as np
+import pandas as pd
+import logging
+from sklearn.metrics.pairwise import cosine_similarity
+
+logger = logging.getLogger(__name__)
+
+def build_hybrid_recommender(df_ratings, df_restaurants):
+    """
+    협업 필터링과 콘텐츠 기반 추천을 결합한 하이브리드 추천 모델 구축
+    
+    Args:
+        df_ratings: 사용자-식당 평점 데이터 (user_id, restaurant_id, score 컬럼 필요)
+        df_restaurants: 식당 메타데이터 (restaurant_id, category_id 등 특성 포함)
+    
+    Returns:
+        callable: 하이브리드 추천 함수
+    """
+    try:
+        logger.info("하이브리드 추천 모델 구축 시작")
+        
+        # 1. 협업 필터링 (사용자-아이템 매트릭스 기반)
+        logger.debug("협업 필터링 모델 구축 중...")
+        # 평점 데이터 확인
+        if df_ratings.empty or 'score' not in df_ratings.columns:
+            raise ValueError("평점 데이터가 비어있거나 필수 컬럼이 없습니다")
+            
+        # 사용자-아이템 평점 매트릭스 생성
+        user_item_matrix = df_ratings.pivot_table(
+            index='user_id', 
+            columns='restaurant_id', 
+            values='score', 
+            fill_value=0
+        )
+        
+        # 협업 필터링 유사도 계산
+        # 메모리 관리를 위해 실제 구현 시 이 부분을 최적화할 수 있음
+        cf_user_similarity = cosine_similarity(user_item_matrix)
+        cf_user_similarity = pd.DataFrame(
+            cf_user_similarity,
+            index=user_item_matrix.index,
+            columns=user_item_matrix.index
+        )
+        
+        # 아이템 유사도 계산
+        cf_item_similarity = cosine_similarity(user_item_matrix.T)
+        cf_item_similarity = pd.DataFrame(
+            cf_item_similarity,
+            index=user_item_matrix.columns,
+            columns=user_item_matrix.columns
+        )
+        
+        logger.debug(f"협업 필터링 모델 구축 완료: {user_item_matrix.shape[0]}명의 사용자, {user_item_matrix.shape[1]}개의 식당")
+        
+        # 2. 콘텐츠 기반 필터링
+        logger.debug("콘텐츠 기반 필터링 모델 구축 중...")
+        # 식당 메타 데이터 준비
+        if df_restaurants.empty:
+            raise ValueError("식당 메타데이터가 비어있습니다")
+        
+        # 콘텐츠 기반 필터링을 위한 특성 선택
+        content_features = ['category_id']
+        
+        # 편의 시설, 주의사항 등의 특성 추가
+        convenience_cols = [col for col in df_restaurants.columns if col.startswith('conv_')]
+        caution_cols = [col for col in df_restaurants.columns if col.startswith('caution_')]
+        
+        content_features.extend(convenience_cols)
+        content_features.extend(caution_cols)
+        
+        # 모든 특성이 존재하는지 확인
+        valid_features = [f for f in content_features if f in df_restaurants.columns]
+        
+        # 유효한 특성이 없으면 카테고리만 사용
+        if not valid_features:
+            if 'category_id' in df_restaurants.columns:
+                valid_features = ['category_id']
+            else:
+                raise ValueError("콘텐츠 기반 필터링에 사용할 특성이 없습니다")
+        
+        # 중복 제거된 식당 데이터 준비
+        restaurant_features = df_restaurants.drop_duplicates('restaurant_id')
+        restaurant_features = restaurant_features.set_index('restaurant_id')
+        
+        # 범주형 변수 원-핫 인코딩
+        categorical_features = ['category_id']
+        for feature in categorical_features:
+            if feature in restaurant_features.columns:
+                # 원-핫 인코딩
+                dummies = pd.get_dummies(restaurant_features[feature], prefix=feature)
+                restaurant_features = pd.concat([
+                    restaurant_features.drop(feature, axis=1),
+                    dummies
+                ], axis=1)
+        
+        # 콘텐츠 기반 유사도 계산
+        # 콘텐츠 특성 선택
+        content_cols = [col for col in restaurant_features.columns 
+                      if any(col.startswith(f"{feature}_") for feature in categorical_features) 
+                      or col in valid_features]
+        
+        if not content_cols:
+            logger.warning("콘텐츠 특성이 없어 기본 특성 사용")
+            content_cols = restaurant_features.columns[:5]  # 첫 5개 컬럼 사용
+        
+        # 유사도 계산
+        content_similarity = cosine_similarity(restaurant_features[content_cols])
+        content_similarity = pd.DataFrame(
+            content_similarity,
+            index=restaurant_features.index,
+            columns=restaurant_features.index
+        )
+        
+        logger.debug(f"콘텐츠 기반 모델 구축 완료: {len(restaurant_features)}개 식당, {len(content_cols)}개 특성")
+        
+        # 3. 하이브리드 추천 함수
+        def hybrid_recommend(user_id, n=15, alpha=0.7):
+            """
+            하이브리드 방식으로 식당 추천
+            
+            Args:
+                user_id: 사용자 ID
+                n: 추천할 식당 수
+                alpha: 협업 필터링 가중치 (0~1), 1-alpha는 콘텐츠 기반 가중치
+                
+            Returns:
+                list: 추천된 식당 ID 리스트
+            """
+            try:
+                # 사용자 ID가 문자열이면 정수로 변환 시도
+                if isinstance(user_id, str):
+                    try:
+                        user_id = int(user_id)
+                    except ValueError:
+                        pass
+                
+                # A. 협업 필터링 점수 계산
+                cf_scores = {}
+                
+                # 사용자가 평점 매트릭스에 있는 경우 (기존 사용자)
+                if user_id in cf_user_similarity.index:
+                    # 1. 유사 사용자 기반 추천
+                    similar_users = cf_user_similarity[user_id].sort_values(ascending=False).index[1:11]  # 자신 제외 상위 10명
+                    
+                    # 2. 유사 사용자들의 평점 가중 평균 계산
+                    user_ratings = user_item_matrix.loc[user_id]
+                    similar_users_ratings = user_item_matrix.loc[similar_users]
+                    user_similarities = cf_user_similarity[user_id].loc[similar_users]
+                    
+                    # 아직 평가하지 않은 식당만 추천 대상
+                    unrated_items = user_ratings[user_ratings == 0].index
+                    
+                    for item in unrated_items:
+                        # 유사 사용자들 중 해당 식당을 평가한 사용자들만 사용
+                        item_ratings = similar_users_ratings[item]
+                        relevant_users = item_ratings[item_ratings > 0].index
+                        
+                        if len(relevant_users) > 0:
+                            # 유사도 가중 평균 계산
+                            relevant_similarities = user_similarities.loc[relevant_users]
+                            relevant_ratings = item_ratings.loc[relevant_users]
+                            
+                            if relevant_similarities.sum() > 0:
+                                cf_scores[item] = (relevant_similarities * relevant_ratings).sum() / relevant_similarities.sum()
+                            else:
+                                cf_scores[item] = relevant_ratings.mean()
+                    
+                    # 3. 아이템 기반 협업 필터링 추가
+                    # 사용자가 이미 평가한 식당
+                    rated_items = user_ratings[user_ratings > 0].index
+                    
+                    for item in unrated_items:
+                        if item not in cf_scores and item in cf_item_similarity.columns:
+                            # 이미 평가한 식당과의 유사성 기반 점수 계산
+                            item_similarities = cf_item_similarity[item].loc[rated_items]
+                            item_ratings = user_ratings.loc[rated_items]
+                            
+                            if item_similarities.sum() > 0:
+                                cf_scores[item] = (item_similarities * item_ratings).sum() / item_similarities.sum()
+                else:
+                    # 신규 사용자는 협업 필터링 점수 없음
+                    logger.debug(f"사용자 {user_id}는 협업 필터링 데이터가 없습니다")
+                
+                # B. 콘텐츠 기반 점수 계산
+                cb_scores = {}
+                
+                # 1. 사용자가 이미 평가한 식당이 있는 경우
+                user_data = df_ratings[df_ratings['user_id'] == user_id]
+                
+                if not user_data.empty:
+                    # 평점이 높은 순으로 사용자가 평가한 식당 정렬
+                    user_favorites = user_data.sort_values('score', ascending=False)
+                    top_restaurants = user_favorites.head(5)['restaurant_id'].tolist()
+                    
+                    # 이미 평가한 식당과 유사한 식당 추천
+                    for rest_id in top_restaurants:
+                        if rest_id in content_similarity.index:
+                            similar_restaurants = content_similarity[rest_id].sort_values(ascending=False)
+                            
+                            for similar_id, similarity in similar_restaurants.items():
+                                if similar_id != rest_id:  # 자기 자신 제외
+                                    if similar_id not in cb_scores:
+                                        cb_scores[similar_id] = 0
+                                    
+                                    # 평가한 식당의 평점과 유사도를 곱하여 점수 계산
+                                    rest_score = user_data[user_data['restaurant_id'] == rest_id]['score'].iloc[0]
+                                    cb_scores[similar_id] += similarity * rest_score
+                else:
+                    # 2. 사용자 평가 데이터가 없는 경우 (신규 사용자)
+                    # 전체 평균 평점으로 인기 식당 추천
+                    popular_restaurants = df_ratings.groupby('restaurant_id')['score'].agg(['mean', 'count'])
+                    popular_restaurants = popular_restaurants[popular_restaurants['count'] >= 5]  # 최소 5개 이상 평가
+                    popular_restaurants['popularity'] = popular_restaurants['mean'] * np.log1p(popular_restaurants['count'])
+                    
+                    for rest_id, row in popular_restaurants.sort_values('popularity', ascending=False).head(20).iterrows():
+                        cb_scores[rest_id] = row['popularity']
+                
+                # C. 하이브리드 점수 계산
+                hybrid_scores = {}
+                
+                # 모든 식당 ID 수집
+                all_restaurant_ids = set(list(cf_scores.keys()) + list(cb_scores.keys()))
+                
+                for rest_id in all_restaurant_ids:
+                    # 협업 필터링 점수 (없으면 0)
+                    cf_score = cf_scores.get(rest_id, 0)
+                    
+                    # 콘텐츠 기반 점수 (없으면 0)
+                    cb_score = cb_scores.get(rest_id, 0)
+                    
+                    # 하이브리드 점수 계산 (알파 가중 평균)
+                    if cf_score > 0 and cb_score > 0:
+                        # 둘 다 점수가 있으면 가중 평균
+                        hybrid_scores[rest_id] = alpha * cf_score + (1 - alpha) * cb_score
+                    elif cf_score > 0:
+                        # 협업 필터링 점수만 있으면 그대로 사용
+                        hybrid_scores[rest_id] = cf_score
+                    elif cb_score > 0:
+                        # 콘텐츠 기반 점수만 있으면 그대로 사용
+                        hybrid_scores[rest_id] = cb_score
+                
+                # 이미 평가한 식당 제외
+                rated_items = df_ratings[df_ratings['user_id'] == user_id]['restaurant_id'].tolist()
+                for item in rated_items:
+                    if item in hybrid_scores:
+                        del hybrid_scores[item]
+                
+                # 점수 기준 상위 n개 식당 추천
+                recommended_items = sorted(
+                    hybrid_scores.items(), 
+                    key=lambda x: x[1], 
+                    reverse=True
+                )[:n]
+                
+                # 식당 ID만 추출
+                recommended_ids = [rest_id for rest_id, _ in recommended_items]
+                
+                # 추천 결과가 부족하면 인기 식당으로 보충
+                if len(recommended_ids) < n:
+                    needed = n - len(recommended_ids)
+                    
+                    # 인기 식당 계산
+                    popular_rest = df_ratings.groupby('restaurant_id')['score'].mean().sort_values(ascending=False)
+                    
+                    # 이미 추천한 식당과 평가한 식당 제외
+                    excluded_ids = set(recommended_ids + rated_items)
+                    additional_ids = [
+                        rest_id for rest_id in popular_rest.index 
+                        if rest_id not in excluded_ids
+                    ][:needed]
+                    
+                    recommended_ids.extend(additional_ids)
+                
+                logger.debug(f"사용자 {user_id}에게 {len(recommended_ids)}개 식당 하이브리드 추천 생성")
+                return recommended_ids
+                
+            except Exception as e:
+                logger.error(f"하이브리드 추천 생성 중 오류: {e}", exc_info=True)
+                
+                # 오류 발생 시 인기 식당 기반 추천으로 대체
+                popular_rest = df_ratings.groupby('restaurant_id')['score'].mean().sort_values(ascending=False)
+                return popular_rest.head(n).index.tolist()
+        
+        logger.info("하이브리드 추천 모델 구축 완료")
+        return hybrid_recommend
+        
+    except Exception as e:
+        logger.error(f"하이브리드 추천 모델 구축 중 오류: {e}", exc_info=True)
+        # 오류 발생 시 기본 추천 함수 반환
+        def fallback_recommend(user_id, n=15, **kwargs):
+            # 평점 기준 인기 식당 추천
+            popular_rest = df_ratings.groupby('restaurant_id')['score'].mean().sort_values(ascending=False)
+            return popular_rest.head(n).index.tolist()
+            
+        return fallback_recommend
+
+
+def generate_hybrid_recommendations(df_ratings, df_restaurants, user_id, n=15, alpha=0.7):
+    """
+    하이브리드 추천 모델을 사용하여 추천 생성
+    
+    Args:
+        df_ratings: 사용자-식당 평점 데이터
+        df_restaurants: 식당 메타데이터
+        user_id: 추천 대상 사용자 ID
+        n: 추천할 식당 수
+        alpha: 협업 필터링 가중치 (0~1)
+        
+    Returns:
+        dict: 추천 결과 딕셔너리
+    """
+    try:
+        # 하이브리드 추천 모델 구축
+        hybrid_recommend = build_hybrid_recommender(df_ratings, df_restaurants)
+        
+        # 추천 생성
+        recommended_items = hybrid_recommend(user_id, n=n, alpha=alpha)
+        
+        # 추천 식당 정보 수집
+        recommendations = []
+        
+        for i, rest_id in enumerate(recommended_items):
+            # 식당 정보 추출
+            rest_data = df_restaurants[df_restaurants['restaurant_id'] == rest_id]
+            if rest_data.empty:
+                continue
+                
+            # 카테고리 ID
+            category_id = int(rest_data['category_id'].iloc[0]) if 'category_id' in rest_data.columns else -1
+            
+            # 점수 정보
+            if 'score' in rest_data.columns:
+                score = float(rest_data['score'].mean())
+            else:
+                # 평점 데이터에서 점수 가져오기
+                rest_ratings = df_ratings[df_ratings['restaurant_id'] == rest_id]
+                score = float(rest_ratings['score'].mean()) if not rest_ratings.empty else 4.0
+            
+            # 추천 식당 정보
+            recommendations.append({
+                "category_id": category_id,
+                "restaurant_id": int(rest_id),
+                "score": score,
+                "predicted_score": score,
+                "composite_score": 5.0 - (i * 0.15)  # 순위에 따라 점수 부여 (5.0~2.75)
+            })
+        
+        # 결과 구성
+        is_new_user = len(df_ratings[df_ratings['user_id'] == user_id]) == 0
+        
+        result_dict = {
+            "user": int(user_id) if isinstance(user_id, (int, float)) else user_id,
+            "is_new_user": is_new_user,
+            "recommendations": recommendations
+        }
+        
+        return result_dict
+        
+    except Exception as e:
+        logger.error(f"하이브리드 추천 생성 중 오류: {e}", exc_info=True)
+        # 기본 결과 반환
+        return {
+            "user": int(user_id) if isinstance(user_id, (int, float)) else user_id,
+            "is_new_user": True,
+            "recommendations": []
+        }
\ No newline at end of file
diff --git a/app/services/model_trainer/train_model.py b/app/services/model_trainer/train_model.py
index 7ac619e..ac3991a 100644
--- a/app/services/model_trainer/train_model.py
+++ b/app/services/model_trainer/train_model.py
@@ -18,6 +18,100 @@
 
 logger = logging.getLogger(__name__)
 
+def enhance_feature_engineering(df_prepared):
+    """
+    향상된 특성 엔지니어링 함수
+    
+    Args:
+        df_prepared: 기본 전처리가 완료된 데이터프레임
+    
+    Returns:
+        df_prepared: 향상된 특성이 추가된 데이터프레임
+    """
+    try:
+        logger.debug("향상된 특성 엔지니어링 시작...")
+        
+        # 1. 카테고리별 희소성 계산
+        category_counts = df_prepared['category_id'].value_counts()
+        total_restaurants = len(df_prepared)
+        category_sparsity = 1 - (category_counts / total_restaurants)
+        
+        # 2. 카테고리 다양성 특성 추가
+        df_prepared['category_diversity_score'] = df_prepared['category_id'].map(
+            category_sparsity.to_dict()
+        ).fillna(0)
+        
+        # 3. 카테고리 인기도 측정
+        category_avg_rating = df_prepared.groupby('category_id')['score'].mean()
+        category_avg_reviews = df_prepared.groupby('category_id')['review'].mean()
+        
+        df_prepared['category_avg_rating'] = df_prepared['category_id'].map(
+            category_avg_rating.to_dict()
+        ).fillna(df_prepared['score'].mean())
+        
+        df_prepared['category_avg_reviews'] = df_prepared['category_id'].map(
+            category_avg_reviews.to_dict()
+        ).fillna(df_prepared['review'].mean())
+        
+        # 4. 식당 인기도 점수
+        df_prepared['popularity_score'] = (
+            df_prepared['score'] * 0.6 +
+            np.log1p(df_prepared['review']) * 0.4
+        )
+        
+        # 5. 리뷰 기반 상호작용 강도 - 가중치 최적화
+        df_prepared['interaction_intensity'] = (
+            df_prepared['review'] * 0.4 + 
+            df_prepared['duration_hours'] * 0.25 + 
+            np.log1p(df_prepared['review']) * 0.35
+        )
+        
+        # 6. 식당 대비 카테고리 성능 (식당이 해당 카테고리 내에서 얼마나 좋은지)
+        df_prepared['rating_vs_category'] = df_prepared['score'] - df_prepared['category_avg_rating']
+        df_prepared['reviews_vs_category'] = df_prepared['review'] / (df_prepared['category_avg_reviews'] + 1)
+        
+        # 7. 복합 특성들
+        # 복합 평점: 카테고리 다양성과 평점 결합
+        df_prepared['composite_rating'] = (
+            df_prepared['score'] * 0.7 + 
+            df_prepared['category_diversity_score'] * 0.2 +
+            df_prepared['rating_vs_category'] * 0.1
+        )
+        
+        # 복합 인기도: 리뷰 수와 운영 시간 결합
+        df_prepared['engagement_score'] = (
+            np.log1p(df_prepared['review']) * 0.7 +
+            (df_prepared['duration_hours'] / 24) * 0.3
+        )
+        
+        # 8. 식당 특성과 카테고리 인기도의 상호작용
+        df_prepared['category_quality_interaction'] = (
+            df_prepared['score'] * df_prepared['category_avg_rating']
+        )
+        
+        # 9. 리뷰 밀도 (시간당 리뷰 수)
+        df_prepared['review_density'] = df_prepared['review'] / (df_prepared['duration_hours'] + 1)
+        
+        # 10. 편의 시설 복합 점수
+        convenience_cols = [col for col in df_prepared.columns if col.startswith('conv_')]
+        if convenience_cols:
+            df_prepared['convenience_score'] = df_prepared[convenience_cols].sum(axis=1)
+        
+        # 11. 리뷰 영향력 비율
+        global_avg_rating = df_prepared['score'].mean()
+        df_prepared['bayesian_rating'] = (
+            (df_prepared['review'] * df_prepared['score'] + 10 * global_avg_rating) /
+            (df_prepared['review'] + 10)
+        )
+        
+        logger.debug("향상된 특성 엔지니어링 완료")
+        return df_prepared
+        
+    except Exception as e:
+        logger.error(f"향상된 특성 엔지니어링 중 오류 발생: {e}", exc_info=True)
+        # 오류가 발생해도 원본 데이터프레임 반환
+        return df_prepared
+    
 def train_model(df_final):
     """
     전처리된 DataFrame을 입력받아 모델 학습과 평가, 앙상블 모델 학습까지 수행합니다.
@@ -62,24 +156,40 @@ def extract_hours_diff(time_str):
             
             df_prepared['duration_hours'] = df_prepared['duration_hours'].apply(extract_hours_diff)
         
-        # 이제 숫자 연산 수행
+        # 기본 피처 생성
         df_prepared['log_review'] = np.log(df_prepared['review'] + 1)
         df_prepared['review_duration'] = df_prepared['review'] * df_prepared['duration_hours']
-        logger.debug("Feature 계산이 완료되었습니다.")
+        
+        # 향상된 특성 엔지니어링 적용
+        df_prepared = enhance_feature_engineering(df_prepared)
+        
+        logger.debug("특성 엔지니어링이 완료되었습니다.")
     except Exception as e:
         logger.error(f"train_model - 피처 생성 오류: {e}", exc_info=True)
         raise e
 
-    # 3. (이미 전처리 과정에서 로그 변환, 상호작용 등 피처 엔지니어링이 이루어졌다고 가정)
-    #    모델 학습에 사용할 피처와 타깃을 설정합니다.
+    # 이미 전처리 과정에서 로그 변환, 상호작용 등 피처 엔지니어링이 이루어졌다고 가정
+    # 3. 모델 학습에 사용할 피처와 타깃을 설정합니다.
     try:
-        model_features = ['review', 'duration_hours', 'conv_WIFI', 'conv_주차', 'caution_예약가능', 'log_review', 'review_duration']
+        # 추가된 특성들을 모델 피처에 포함
+        model_features = [
+            'review', 'duration_hours', 'conv_WIFI', 'conv_주차', 
+            'caution_예약가능', 'log_review', 'review_duration',
+            'category_diversity_score', 'interaction_intensity', 
+            'composite_rating', 'popularity_score', 'rating_vs_category',
+            'reviews_vs_category', 'engagement_score', 'category_quality_interaction',
+            'review_density', 'bayesian_rating'
+        ]
+        
+        # 존재하는 피처만 선택 (일부 특성이 생성되지 않을 수 있음)
+        model_features = [f for f in model_features if f in df_prepared.columns]
+        
         target = 'score'
         X = df_prepared[model_features]
         y = df_prepared[target]
-        logger.debug(f"Featuer 및 타깃 설정이 완료되었습니다: {model_features}")
+        logger.debug(f"Feature 및 타깃 설정이 완료되었습니다: {model_features}")
     except Exception as e:
-        logger.error(f"train_model - Featuer 및 타깃 설정 오류: {e}", exc_info=True)
+        logger.error(f"train_model - Feature 및 타깃 설정 오류: {e}", exc_info=True)
         raise e
         
     # 4. 특성 스케일링 및 데이터 분할
@@ -128,6 +238,4 @@ def extract_hours_diff(time_str):
         "stacking_reg": stacking_reg,
         "model_features": model_features,
         "df_model": df_prepared
-    }
-
-
+        }
\ No newline at end of file
diff --git a/catboost_info/catboost_training.json b/catboost_info/catboost_training.json
index 1d027b8..ea59c2d 100644
--- a/catboost_info/catboost_training.json
+++ b/catboost_info/catboost_training.json
@@ -1,104 +1,104 @@
 {
 "meta":{"test_sets":[],"test_metrics":[],"learn_metrics":[{"best_value":"Min","name":"RMSE"}],"launch_mode":"Train","parameters":"","iteration_count":100,"learn_sets":["learn"],"name":"experiment"},
 "iterations":[
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-{"learn":[1.314911341],"iteration":2,"passed_time":0.0008645901795,"remaining_time":0.02795508247},
-{"learn":[1.310107525],"iteration":3,"passed_time":0.001136175663,"remaining_time":0.02726821592},
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-{"learn":[1.295985705],"iteration":7,"passed_time":0.002328310103,"remaining_time":0.02677556619},
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diff --git a/catboost_info/learn/events.out.tfevents b/catboost_info/learn/events.out.tfevents
index a0fd12f..9ee1256 100644
Binary files a/catboost_info/learn/events.out.tfevents and b/catboost_info/learn/events.out.tfevents differ
diff --git a/catboost_info/learn_error.tsv b/catboost_info/learn_error.tsv
index 4735f22..30b8d55 100644
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+67	0.04853541042
+68	0.04836143154
+69	0.04783057962
+70	0.04764459246
+71	0.04730315287
+72	0.04693227517
+73	0.04634447701
+74	0.04565981491
+75	0.04528190729
+76	0.04486598408
+77	0.04442669194
+78	0.04395922486
+79	0.04369273201
+80	0.04348321459
+81	0.04321093871
+82	0.04295930351
+83	0.04256577847
+84	0.04231569496
+85	0.04202583115
+86	0.04139549667
+87	0.04134324962
+88	0.04083942929
+89	0.04077225011
+90	0.04040815038
+91	0.04003502673
+92	0.03974815226
+93	0.03955683456
+94	0.0393602484
+95	0.03901075876
+96	0.038886721
+97	0.03857550898
+98	0.03832786438
+99	0.03811578664
diff --git a/catboost_info/time_left.tsv b/catboost_info/time_left.tsv
index 10778e5..fe804e3 100644
--- a/catboost_info/time_left.tsv
+++ b/catboost_info/time_left.tsv
@@ -1,101 +1,101 @@
 iter	Passed	Remaining
-0	0	31
-1	0	28
-2	0	27
-3	1	27
-4	1	26
-5	1	27
-6	2	26
-7	2	26
-8	2	26
-9	2	26
-10	3	26
-11	3	26
-12	3	25
-13	4	25
-14	4	25
-15	4	24
-16	5	24
-17	5	24
-18	5	24
-19	6	24
-20	6	23
-21	6	23
-22	7	23
-23	7	23
-24	7	23
-25	8	22
-26	8	22
-27	8	22
-28	9	22
-29	9	21
-30	9	21
-31	9	21
-32	10	20
-33	10	20
-34	10	20
-35	11	20
-36	11	19
-37	11	19
-38	12	19
-39	12	18
-40	12	18
-41	13	18
-42	13	17
-43	13	17
-44	14	17
-45	14	16
-46	14	16
-47	15	16
-48	15	16
-49	15	15
-50	16	15
-51	16	15
-52	16	14
-53	17	14
-54	17	14
-55	17	13
-56	18	13
-57	18	13
-58	18	12
-59	18	12
-60	19	12
-61	19	11
-62	19	11
-63	19	11
-64	20	10
-65	20	10
-66	20	10
-67	21	9
-68	21	9
-69	21	9
-70	22	9
-71	22	8
-72	23	8
-73	23	8
-74	23	7
-75	24	7
-76	24	7
-77	24	6
-78	24	6
-79	25	6
-80	25	6
-81	26	5
-82	26	5
-83	26	5
-84	26	4
-85	27	4
-86	27	4
-87	27	3
-88	27	3
-89	28	3
-90	28	2
-91	28	2
-92	29	2
-93	29	1
-94	29	1
-95	30	1
-96	30	0
-97	30	0
-98	31	0
-99	31	0
+0	0	57
+1	1	53
+2	1	57
+3	2	56
+4	2	54
+5	3	52
+6	3	51
+7	4	51
+8	4	50
+9	5	49
+10	6	49
+11	6	49
+12	7	48
+13	7	48
+14	8	47
+15	9	47
+16	9	46
+17	10	46
+18	10	45
+19	12	48
+20	12	47
+21	13	46
+22	13	46
+23	14	45
+24	14	44
+25	15	44
+26	16	43
+27	16	42
+28	16	41
+29	17	40
+30	17	39
+31	18	39
+32	19	38
+33	19	38
+34	20	37
+35	20	37
+36	21	36
+37	21	35
+38	23	36
+39	23	35
+40	24	34
+41	24	34
+42	25	33
+43	25	32
+44	26	32
+45	26	31
+46	27	30
+47	27	30
+48	28	29
+49	28	28
+50	29	28
+51	30	27
+52	30	27
+53	31	26
+54	31	25
+55	32	25
+56	32	24
+57	33	24
+58	33	23
+59	34	22
+60	34	22
+61	35	21
+62	36	21
+63	36	20
+64	37	20
+65	37	19
+66	38	18
+67	39	18
+68	39	17
+69	40	17
+70	40	16
+71	41	16
+72	41	15
+73	42	14
+74	42	14
+75	43	13
+76	44	13
+77	44	12
+78	45	12
+79	45	11
+80	46	10
+81	46	10
+82	47	9
+83	47	9
+84	48	8
+85	48	7
+86	49	7
+87	49	6
+88	50	6
+89	51	5
+90	51	5
+91	52	4
+92	52	3
+93	53	3
+94	54	2
+95	55	2
+96	55	1
+97	56	1
+98	56	0
+99	57	0
diff --git a/requirements.txt b/requirements.txt
index 95b07bc..2bcba3b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -13,4 +13,5 @@ pymysql
 catboost==1.2.7
 asyncio
 sshtunnel
-pymongo
\ No newline at end of file
+pymongo
+optuna
\ No newline at end of file
diff --git a/storage/input_json/user/preprocessed_user_features.csv b/storage/input_json/user/preprocessed_user_features.csv
index 0771c14..ec4be24 100644
--- a/storage/input_json/user/preprocessed_user_features.csv
+++ b/storage/input_json/user/preprocessed_user_features.csv
@@ -1,1005 +1,1005 @@
 user_id,max_price,min_price,category_1,category_2,category_3,category_4,category_5,category_6,category_7,category_8,category_9,category_10,category_11,category_12,total_reservations,completed_reservations,reservation_completion_rate,total_likes,like_to_reservation_ratio
-1,286141,183646,0,1,1,0,0,0,0,0,0,0,1,0,8.0,8,1.0,6,0.75
-2,444065,170838,0,0,0,0,0,1,1,0,0,1,0,0,4.0,4,1.0,6,1.5
-3,395779,79971,0,0,1,1,0,0,1,0,0,0,0,0,2.0,2,1.0,4,2.0
-4,200419,82759,1,0,0,0,0,0,1,0,0,0,1,0,8.0,8,1.0,6,0.75
-5,341399,147700,0,0,1,0,0,1,0,0,1,0,0,0,2.0,2,1.0,4,2.0
-6,304138,159625,0,1,1,0,1,0,0,0,0,0,0,0,10.0,10,1.0,0,0.0
-7,338378,151460,1,0,0,0,0,1,0,0,0,0,0,1,2.0,2,1.0,12,6.0
-8,376584,66889,0,0,0,0,0,0,1,1,0,1,0,0,4.0,4,1.0,2,0.5
-9,393014,91158,0,0,0,1,0,0,0,0,1,0,1,0,2.0,2,1.0,4,2.0
-10,267487,170163,0,0,1,1,0,0,0,1,0,0,0,0,,0,0.0,8,5.0
-11,470524,29163,1,0,0,0,1,0,0,0,0,0,1,0,6.0,6,1.0,4,0.67
-12,446869,142652,0,1,0,0,0,0,0,1,0,0,1,0,6.0,6,1.0,4,0.67
-13,351840,185816,0,1,1,0,0,0,0,0,0,0,0,1,8.0,8,1.0,6,0.75
-14,332509,38329,0,0,0,0,0,0,1,1,1,0,0,0,2.0,2,1.0,6,3.0
-15,266941,59156,0,1,0,0,0,0,1,0,0,0,0,1,12.0,12,1.0,10,0.83
-16,337197,194701,1,1,0,0,0,0,0,0,1,0,0,0,2.0,2,1.0,8,4.0
-17,481858,85248,0,0,1,1,0,0,0,0,0,1,0,0,2.0,2,1.0,6,3.0
-18,282926,36257,1,0,0,0,1,0,0,0,0,0,1,0,2.0,2,1.0,10,5.0
-19,327603,197901,1,0,0,0,0,0,0,0,1,0,0,1,8.0,8,1.0,4,0.5
-20,292092,166283,0,1,0,0,0,0,0,1,0,1,0,0,10.0,10,1.0,8,0.8
-21,353557,28934,0,0,1,0,0,0,0,1,0,0,0,1,2.0,2,1.0,2,1.0
-22,319083,167451,0,1,1,0,0,0,0,0,0,1,0,0,6.0,6,1.0,8,1.33
-23,295629,72379,0,0,1,0,0,0,0,1,0,0,0,1,2.0,2,1.0,8,4.0
-24,279131,95305,0,0,0,1,0,0,0,0,0,1,0,1,4.0,4,1.0,16,4.0
-25,233947,106565,0,0,1,0,0,1,0,0,0,0,0,1,6.0,6,1.0,4,0.67
-26,288981,180902,1,0,0,0,0,1,0,0,0,1,0,0,2.0,2,1.0,6,3.0
-27,254815,187129,1,1,0,0,0,0,0,0,1,0,0,0,2.0,2,1.0,8,4.0
-28,403490,169312,0,0,0,1,0,0,1,0,0,0,0,1,10.0,10,1.0,26,2.6
-29,403053,151914,0,0,0,0,0,0,1,0,1,0,0,1,4.0,4,1.0,6,1.5
-30,227881,133174,0,0,0,1,0,1,0,0,0,0,1,0,6.0,6,1.0,4,0.67
-31,207906,88090,0,0,1,0,0,0,0,0,1,1,0,0,2.0,2,1.0,10,5.0
-32,270292,97183,0,1,1,0,0,0,0,0,0,1,0,0,4.0,4,1.0,6,1.5
-33,403876,89280,0,0,0,0,0,0,0,1,1,0,1,0,6.0,6,1.0,4,0.67
-34,312422,61746,0,0,1,1,0,0,0,0,0,1,0,0,6.0,6,1.0,8,1.33
-35,369064,109942,0,0,0,0,0,1,0,0,0,1,1,0,6.0,6,1.0,8,1.33
-36,493736,110323,0,1,1,0,0,0,1,0,0,0,0,0,2.0,2,1.0,2,1.0
-37,469280,35055,1,0,0,1,0,0,0,0,0,0,0,1,2.0,2,1.0,4,2.0
-38,484502,61187,1,0,0,0,0,0,0,0,1,1,0,0,4.0,4,1.0,8,2.0
-39,311737,164628,0,0,0,1,0,0,0,0,0,1,1,0,2.0,2,1.0,12,6.0
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-86,398628,185050,0,1,0,0,1,0,0,1,0,0,0,0,8.0,8,1.0,4,0.5
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