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🎬 | 🤝 Usecollaborative filtering to predict user preferences, assuming similar users will like similar movies, and leverageSingular Value Decomposition (SVD) from Numpy for precise matrix factorization and accurate recommendations📊 |    |
| 🎥 Movie Recommendation with Genres | 🔍 Creates movie embeddings usingDoc2Vec, capturing genre and characteristic nuances, and leverages VectorDB for efficient storage and querying, enabling accurate genre classification and personalized movie recommendations throughsimilarity searches🎥 |  |
| 🛍️ Product Recommender using Collaborative Filtering and LanceDB | 📈 UsingCollaborative Filtering andLanceDB 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 forArxiv papers usingLanceDB, and benchmarks its performance against traditional keyword-based search onNomic's Atlas, to demonstrate the power of semantic search in finding relevant research papers📚 | |
| Food Recommendation System🍴 | 🍔 Build a food recommendation system withLanceDB, featuring vector-based recommendations, full-text search, hybrid search, and reranking model integration for personalized and accurate food suggestions👌 | |