To build a recommendation system with a document database, you start by structuring data to capture user preferences and item features, then use query patterns to generate recommendations. Document databases like MongoDB or Couchbase store flexible, JSON-like documents, which work well for representing user profiles (e.g., liked items, viewed content) and item metadata (e.g., tags, categories). For example, a user document might include fields like user_id, liked_movies (an array of movie IDs), and preferred_genres, while a movie document could have title, genres, and tags. This schema-less design allows easy updates as user behavior or item attributes evolve.
Recommendations are typically generated using collaborative filtering, content-based filtering, or hybrid approaches. Collaborative filtering in a document database might involve querying for users with similar interaction histories. For instance, find users who liked the same movies as the target user and recommend movies those users also enjoyed. Content-based filtering uses item attributes: if a user prefers sci-fi movies, query for movies tagged “sci-fi” and rank them by popularity or release date. Hybrid approaches combine both methods—for example, using collaborative filtering to find similar users, then filtering results by content tags. To improve performance, precompute embeddings (e.g., TF-IDF vectors for item descriptions) and store them in documents for fast similarity searches using database-native vector operations or extensions like MongoDB’s $dotProduct.
Implementation involves indexing, query optimization, and caching. Create indexes on fields like liked_movies or tags to speed up lookups. For example, in MongoDB, use an aggregation pipeline to match users with overlapping liked_movies, unwind the array, and group by common items. For content-based recommendations, use text indexes on genres or vector indexes on precomputed embeddings. To handle real-time updates, trigger a background job to refresh user recommendations when new interactions occur. Cache frequent recommendations in the document database itself (e.g., storing top 10 suggestions in a recommendations array within the user document) to reduce query latency. Tools like Redis can also cache hot recommendations for high-throughput systems.