Item-to-item recommendations help users discover products similar to what they are currently viewing or have purchased. This technique powers features like “You might also like” and “Frequently bought together” on e-commerce platforms. These recommendations:
Increase customer engagement by surfacing relevant products.
Boost conversion rates by promoting co-purchased or similar items.
Enhance personalization by learning from user behavior patterns.
Kumo AI can generate rich item embeddings optimized for co-purchases, not just semantic similarity (e.g., recommending cereal when someone buys milk instead of just different types of milk). It balances signals from purchases, views, and interactions to improve recommendations beyond simple text matching.
Batch Recommendations in a Key-Value StoreA common way to serve item recommendations is to precompute them and store them in a key-value store for low-latency retrieval:
At runtime, the application retrieves recommendations instantly for a given product.Using Embeddings for Similarity ScoresInstead of retrieving static predictions, we can use dot product similarity between embeddings to dynamically compute item similarity scores. This enables:
Personalized recommendations based on the user’s session.
Real-time filtering to exclude items already viewed by the user.
Example workflow:
Cache top-10 recommendations for each item in a key-value store.
Store LHS and RHS embeddings in a vector database.
During a session:
Show cached recommendations first.
If the user exhausts cached items, backfill recommendations dynamically using dot product similarity.
This hybrid approach balances speed and flexibility.