Recommender Systems: Personalized Discovery🍿📺

Deliver personalized experiences with Recommender Systems. 🎁

Technical Overview📜

🔍️ LanceDB's powerful vector database capabilities can efficiently store and query item embeddings. Recommender Systems can utilize it and provide personalized recommendations based on user preferences 🤝 and item features 📊 and therefore enhance the user experience.🗂️

Recommender System	Description	Links
Movie Recommender System🎬	🤝 Use collaborative filtering to predict user preferences, assuming similar users will like similar movies, and leverage Singular Value Decomposition (SVD) from Numpy for precise matrix factorization and accurate recommendations📊
🎥 Movie Recommendation with Genres	🔍 Creates movie embeddings using Doc2Vec, capturing genre and characteristic nuances, and leverages VectorDB for efficient storage and querying, enabling accurate genre classification and personalized movie recommendations through similarity searches🎥
🛍️ Product Recommender using Collaborative Filtering and LanceDB	📈 Using Collaborative Filtering and LanceDB to analyze your past purchases, recommends products based on user's past purchases. Demonstrated with the Instacart dataset in our example🛒
🔍 Arxiv Search with OpenCLIP and LanceDB	💡 Build a semantic search engine for Arxiv papers using LanceDB, and benchmarks its performance against traditional keyword-based search on Nomic's Atlas, to demonstrate the power of semantic search in finding relevant research papers📚
Food Recommendation System🍴	🍔 Build a food recommendation system with LanceDB, featuring vector-based recommendations, full-text search, hybrid search, and reranking model integration for personalized and accurate food suggestions👌