Building a Mood-Based Movie Recommendation Engine with Voyage-4-nano, Hugging Face, and MongoDB Atlas Vector Search

A tutorial explores building a mood-based movie recommendation engine using advanced technologies like Voyage-4-nano, Hugging Face, and MongoDB Atlas Vector Search. Unlike traditional movie searches based on genre or actor, this approach enables users to find films corresponding to their emotional state, such as needing something uplifting or a movie that will make them cry. The system leverages semantic search, using the Voyage-4-nano embedding model to convert text into embeddings, which are then stored and queried using MongoDB Atlas Vector Search. The architecture integrates Sentence Transformers for efficient embedding management, allowing users to input mood descriptions that are semantically matched against movie plot descriptions. The tutorial details the setup and implementation of the system components, including configuring the development environment, indexing data, and running a FastAPI application for mood-based search. It emphasizes the benefits of using different embedding dimensions for balancing semantic quality, storage efficiency, and vector search latency, ultimately enhancing the movie recommendation process by understanding nuanced user intent.