user-to-item

Personalized recommendations construct a user vector from interaction sequences—purchases, clicks, views—weighted by signal strength and optional time decay. Vector similarity then ranks items matching that profile, delivering relevant results tailored to individual preference patterns.

Vector search retrieves and ranks items by similarity to a user profile with optional filters like category constraints or exclusions. This approach scales to large catalogs and supports fast, filtered ranking for e-commerce, content feeds, and media apps.

Cold-start strategies employ popular-item baselines or exploration modes to surface content when user history is minimal. These approaches reduce friction for new users while gathering interaction signals to refine profile accuracy over time.

Ranking combines vector similarity for relevance with optional exploration mechanisms to introduce diversity. Weighted signals and filters ensure top-k results match user preference while reducing repetitive or overly similar recommendations.

Yes. The skill handles dynamic feeds across e-commerce, streaming, and media platforms by maintaining per-user profiles and fast vector search with ranking. It adapts to evolving user interests through continuous interaction signals.

The skill uses item embeddings and Milvus for fast vector search infrastructure. User profiles are compact vectors built from weighted interactions, enabling efficient similarity queries against large embedding collections.