item-to-item

Content-based recommendations use embeddings to identify similar items by comparing their text or image features. Generate embeddings for item descriptions and metadata, store them in a vector database like Milvus, then query for items most similar to a source item based on embedding distance.

Yes, cross-category recommendations identify similar items across different product categories by comparing embeddings rather than category tags. Filter results by business rules like stock availability or price range to surface relevant cross-category suggestions.

Filter out the source item and apply business logic to rank results by embedding similarity while enforcing constraints like minimum diversity, stock status, and shop availability. This balances relevance with variety in the final recommendation set.

Text embeddings alone are sufficient for similarity search, but optional image features enhance recommendations by capturing visual attributes. Combine text and image embeddings for richer content-based matching across products, articles, and media.

Vector stores like Milvus index embeddings for fast similarity search, enabling instant 'more like this' queries across large catalogs. Query with an embedded source item and retrieve the nearest neighbors ranked by cosine or Euclidean distance.