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feedback_loop.py
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feedback_loop.py
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import sys
import random
import chromadb
from sqlalchemy import select, and_, func
from db_utils.azure_db import get_session
from db_utils.user import User, Watched, Rating, RecommendationsHistory
from db_utils.movie import Movie
from main import get_movie_history_and_ratings, generate_candidates, get_recommendations
import datetime
import numpy as np
import json
# Initialize database session
# # stmt = select(Watched.movie_id, Rating.rating).\
# # outerjoin(Rating, and_(Watched.user_id == Rating.user_id, Watched.movie_id == Rating.movie_id)).\
# # where(Watched.user_id == 1).where(Watched.date_added <= ).order_by(Watched.date_added)
# stmt = select(RecommendationsHistory.recommendations).\
# where(RecommendationsHistory.user_id == 1)
# result = session.execute(stmt)
# for row in result:
# print(row)
session = get_session()
def detect_feedback_loops(session, threshold=0.7):
# Query to fetch all recommendations
query = select(RecommendationsHistory.recommendations)
results = session.execute(query).fetchall()
# Flatten the lists and count occurrences of each movie
movie_recommendation_counts = {}
for movie_list in results:
if movie_list[0]:
# print(result)
# print(result[0])
# print(movie_list)
# print(movie_list[0])
movie_titles = movie_list[0].split(',')
for movie_id in movie_titles:
# print(movie_id)
movie_recommendation_counts[movie_id] = movie_recommendation_counts.get(movie_id, 0) + 1
# Find the maximum number of recommendations for a single movie
max_recommendations = max(movie_recommendation_counts.values(), default=0)
# Identify movies that are recommended too often
feedback_loops_detected = []
for movie_id, count in movie_recommendation_counts.items():
if count > threshold * max_recommendations:
# print(count)
feedback_loops_detected.append(movie_id)
return feedback_loops_detected
# Setting a threshold for feedback loop detection
threshold = 0.7
# Detect feedback loops
feedback_loops = detect_feedback_loops(session, threshold)
if feedback_loops:
print("Feedback loops detected in the following movies:")
for movie_id in feedback_loops:
print(f"Movie ID: {movie_id}")
else:
print("No significant feedback loops detected.")
# Close the session
session.close()