Project Beta
AI-Powered Recommendation Engine
Project Overview
A hybrid recommendation system combining collaborative filtering, content-based filtering, and machine learning models to generate highly personalized movie recommendations in sub-second response times.
Problem Statement
User retention on media streaming sites depends heavily on high-quality content recommendations. Collaborative filtering alone suffers from the "cold start" problem for new users and items, while content filtering lacks user personalization.
The Solution
Built a hybrid recommendation system combining content-based filtering (TF-IDF on movie metadata) and collaborative filtering (Matrix Factorization) to recommend personalized content. Used LightGBM for ranking the recommendation candidate list.
Key Features
- Hybrid recommendation engine utilizing collaborative and content filtering.
- TF-IDF similarity search for mapping content metadata tags.
- High-performance prediction API using FastAPI.
- Personalized user profiles updating dynamically upon likes.
- Redis cache layers to optimize recommendation candidate lookup.
System Architecture
Python-based machine learning pipeline with pandas, scikit-learn, and LightGBM. Fast API endpoint utilizing Redis caching to serve recommendations. Responsive React.js frontend for browsing catalog and real-time content feedback.
Challenges & Solutions
Reducing model inference time on runtime recommendations to prevent API page lag.
Results & Impact
Optimized inference pipelines with Redis cache layers, reducing response times to sub-200ms. Increased prediction relevance scores by 18% compared to single-algorithm models.