Three Recommendation Approaches
Collaborative Filtering
This approach finds users with similar behavior patterns and recommends items that similar users liked. The implementation uses matrix factorization (SVD) from the Surprise library, which handles sparse user-item matrices efficiently. It works well when you have sufficient user interaction data (ratings, purchases, views) but struggles with new users who have no history (the cold-start problem).
Content-Based Filtering
This approach recommends items similar to what the user has already interacted with. Item similarity is computed from item features -- product descriptions, categories, tags, and metadata. The implementation uses TF-IDF vectorization and cosine similarity. It solves the cold-start problem for new users (just need a few interactions) but can create filter bubbles where recommendations get repetitive.
Hybrid Approach
The hybrid model combines both approaches with configurable weights. New users get content-based recommendations. As interaction data accumulates, the system gradually shifts toward collaborative filtering. The weighting is automatic based on the user's interaction count -- no manual tuning needed. This is the recommended approach for most applications.
API and Integration
The REST API accepts a user ID and returns ranked recommendations with confidence scores. Average response time is under 100ms for catalogs with up to 100K items. The API supports filtering by category, price range, and availability. Batch endpoints generate recommendations for all users at once for email campaigns or pre-caching.
