ClusterRAG: Cluster-Based Collaborative Filtering for Personalized Retrieval-Augmented Generation
The paper introduces ClusterRAG, a novel approach for Personalized Retrieval-Augmented Generation (RAG). It emphasizes the importance of collaborative signals from similar users to improve document retrieval and generation. Extensive experiments demonstrate that ClusterRAG outperforms existing methods by leveraging user profiles and clustering techniques.
- ▪ClusterRAG utilizes density-based clustering to organize users into semantically coherent groups.
- ▪The method performs retrieval at both the cluster and document levels, enhancing personalization.
- ▪Experiments on the LaMP benchmark show that ClusterRAG consistently yields superior performance across various tasks.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.18769 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | au9jYfQM8emI · 2 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Information Retrieval arXiv:2605.18769 (cs) [Submitted on 14 Apr 2026] Title:ClusterRAG: Cluster-Based Collaborative Filtering for Personalized Retrieval-Augmented Generation Authors:Gibson Nkhata, Uttamasha Anjally Oyshi, Quan Mai, Susan Gauch View a PDF of the paper titled ClusterRAG: Cluster-Based Collaborative Filtering for Personalized Retrieval-Augmented Generation, by Gibson Nkhata and 3 other authors View PDF HTML (experimental) Abstract:Personalized Retrieval-Augmented Generation (RAG) relies on accurately selecting user-relevant documents. In practice, existing RAG approaches often suffer from high retrieval costs and overlook that collaborative signals from similar users can enhance personalized generation for the current user.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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