ClusterRAG: Cluster-Based Collaborative Filtering for Personalized Retrieval-Augmented Generation (2026.acl-long)
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| Challenge: | Existing approaches to Personalized Retrieval-Augmented Generation (RAG) ignore long-term user information and inter-user relationships when constructing retrieval contexts, limiting personalization and the ability to leverage analogous users' knowledge for improved generation quality. |
| Approach: | They propose a Cluster-Based Collaborative Filtering for Personalized Retrieval-Augmented Generation that organizes users into semantically coherent clusters and performs retrieval at both the cluster and document levels via cluster-level similarity and fine-grained ranking. |
| Outcome: | Extensive experiments on the LaMP benchmark show that ClusterRAG integrates seamlessly with different dense retrievers and rankers, and remains effective when paired with both fine-tuned and zero-shot language models. |
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Haoyu Huang, Yongfeng Huang, Yang Junjie, Zhenyu Pan, Yongqiang Chen, Kaili Ma, Hongzhi Chen, James Cheng
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