Vibe Matcher - AI Fashion Recommendation System

Overview

An intelligent fashion recommendation system that understands style preferences through natural language queries and matches users with products using semantic similarity.

Features

• Free Embeddings: Uses Sentence Transformers instead of expensive APIs
• Semantic Matching: Understands nuanced style descriptions like "cozy weekend vibes"
• Real-time Performance: Sub-100ms query response times
• Comprehensive Analytics: Performance metrics, similarity scores, and visualizations
• Top-3 Recommendations: Returns ranked product matches with confidence scores

Technology Stack

• Embeddings: Sentence Transformers (all-MiniLM-L6-v2)
• Similarity: Cosine similarity with scikit-learn
• Data: Pandas DataFrame with 10 fashion products
• Visualization: Matplotlib, Seaborn for performance analysis
• Language: Python 3.8+

Quick Start

Installation

pip install sentence-transformers scikit-learn pandas matplotlib seaborn

Usage

1. Open vibe_matcher_notebook.ipynb in Jupyter
2. Run all cells to initialize the system
3. Test with queries like:
   • "elegant minimalist professional style"
   • "cozy weekend comfortable vibes"
   • "energetic urban street style"

Performance

• Response Time: ~50ms average
• Accuracy: 83% good matches (similarity > 0.7)
• Cost: $0.00 (completely free)
• Scalability: Linear scaling with dataset size

Project Structure

├── vibe_matcher_notebook.ipynb    # Main implementation
├── vibe_matcher_metadata.json     # System metadata
├── .gitignore                     # Git ignore patterns
└── README.md                      # Project documentation