user-to-item

What problem does it solve?

This skill enables personalized recommendations for individual users based on their history, improving engagement and relevance across dynamic feeds.

Core Features & Use Cases

• User-profile construction: Build a compact user vector from a sequence of interactions using weighted signals (purchase, click, view) and optional time decay to reflect evolving interests.
• Personalized ranking via vector search: Retrieve and rank items by similarity to the user profile with optional filters (exclude items, category constraints) to deliver relevant results.
• Cold-start and exploration: Employ strategies like popular-item baselines or exploration to surface new content for users with minimal history, reducing cold-start friction.
• Use cases: Ideal for e-commerce “For You” product recommendations, streaming/content feeds, and any scenario requiring user-centric content ranking.

Quick Start

Provide a user_id and a short history of interactions to generate a personalized recommendation list. For example, request top_k recommendations for user_123 based on recent purchases and views, and the system will return a ranked set of items tailored to that user’s inferred preferences.