# recommendation

> Browse 3 items tagged with recommendation.

## Items

- [Bloomreach Discovery](https://vectordb.ever.works/items/bloomreach-discovery) — Commerce-focused platform bundling search and recommendations into a single system. Uses embeddings and relevance models under the hood but presents them as APIs and tools for merchandisers, eliminating the need for a separate vector database in e-commerce setups.
- [Reinforcement Routing on Proximity Graph for Efficient Recommendation](https://vectordb.ever.works/items/reinforcement-routing-on-proximity-graph-for-efficient-recommendation) — TOIS 2023 paper proposing reinforcement learning-based routing on proximity graphs for efficient recommendation, applying graph traversal optimization to recommendation systems using vector-based item representations.
- [Cold Start Problem](https://vectordb.ever.works/items/cold-start-problem) — The challenge of making recommendations or performing similarity search when there is insufficient historical data for new users, items, or embeddings. In vector databases and RAG systems, cold start affects new documents without usage data, requiring strategies like content-based filtering and hybrid approaches.

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_Canonical page: https://vectordb.ever.works/tags/recommendation_