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home / papers / 2026 / ClusterRAG: Cluster-Based Collaborative Filtering for Personalized Retrieval-Augmented Generation

ClusterRAG: Cluster-Based Collaborative Filtering for Personalized Retrieval-Augmented Generation

Paper · 2026 · Gibson Nkhata, Uttamasha Anjally Oyshi, Quan Mai et al.

Summary

Personalized RAG systems usually build context in one of two ways. Some use only the current user's own history, which can be thin or noisy. Others ignore user history entirely. ClusterRAG takes a middle path inspired by collaborative filtering: it also draws evidence from users who behave similarly. Each user becomes one vector by averaging ColBERTv2 embeddings of their profile documents. HDBSCAN then groups users into density-based cohorts, so neighbour search runs inside a cluster rather than over all user pairs. A cluster-level ranking matrix, scored with ColBERTv2, orders users within each cohort. Documents from the top-k neighbours are then ranked to fill prompts in one of three modes: collaborative, user-only or hybrid. On the LaMP benchmark, the hybrid mode performed best across tasks; it pairs the target user's own profile with material from the closest neighbours. The pipeline works with several retrievers and with both fine-tuned and zero-shot generators. This matters because it gives a cheap, model-agnostic way to enrich sparse user profiles without changing model parameters.

Key points

  • Clustering and outliers: users are grouped with HDBSCAN, so the number of clusters does not need to be fixed in advance. Outlier users are placed together in a dedicated cluster.
  • Retrievers compared: ColBERT, Contriever, BGE, BM25, plus Recency and Random baselines. ColBERTv2 is also used to build user embeddings and to compute similarities within clusters.
  • Generators tested: FlanT5-base and FlanT5-XXL (fine-tuned seq2seq) and Qwen2-7B-Instruct (zero-shot, decoder-only). None required model-specific adaptation.
  • Evaluation: on LaMP, which includes tasks such as LaMP-1 citation identification and LaMP-5 scholarly title generation, the hybrid prompt mode gave the best overall results.
  • Limitations: the evaluation covers only English text-only data, and clustering is done offline. The code is available at github.com/gnkhata/ClusterRAG.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of Arkansas at FayettevilleFayetteville · United States3 works · 272 citations

Also on the byline: Walmart.

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksInformation Systems · 1,437 works

🧭 Topics 1Research subjects and languages the work is about.Information retrieval & search · 473 works

Cite

Cite as Gibson Nkhata, Uttamasha Anjally Oyshi, Quan Mai and Susan Gauch. 2026. ClusterRAG: Cluster-Based Collaborative Filtering for Personalized Retrieval-Augmented Generation. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.940

@inproceedings{nkhata2026clusterrag,
  title = {{ClusterRAG: Cluster-Based Collaborative Filtering for Personalized Retrieval-Augmented Generation}},
  author = {Gibson Nkhata, Uttamasha Anjally Oyshi, Quan Mai and Susan Gauch},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.940},
  url = {https://doi.org/10.18653/v1/2026.acl-long.940}
}

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