[2601.08816] MemRec: Collaborative Memory-Augmented Agentic Recommender System

Computer Science > Information Retrieval arXiv:2601.08816 (cs) [Submitted on 13 Jan 2026 (v1), last revised 28 Apr 2026 (this version, v3)] Title:MemRec: Collaborative Memory-Augmented Agentic Recommender System Authors:Weixin Chen, Yuhan Zhao, Jingyuan Huang, Zihe Ye, Clark Mingxuan Ju, Tong Zhao, Neil Shah, Li Chen, Yongfeng Zhang View a PDF of the paper titled MemRec: Collaborative Memory-Augmented Agentic Recommender System, by Weixin Chen and Yuhan Zhao and Jingyuan Huang and Zihe Ye and Clark Mingxuan Ju and Tong Zhao and Neil Shah and Li Chen and Yongfeng Zhang View PDF HTML (experimental) Abstract:The evolution of recommender systems has shifted from traditional collaborative filtering to LLM-based agentic systems, which rely on semantic user and item memories to make predictions. However, existing agents maintain these memories in isolation. This overlooks crucial collaborative signals, such as user-item co-engagements and peer relationships across the community, which significantly limits their ability to uncover hidden preferences and accurately infer user needs, particularly for data-sparse users. To bridge this gap, we introduce collaborative memory, a paradigm that connects isolated semantics to enable the sharing of relational insights. Yet, naively utilizing collaborative memory causes severe context overload and introduces noise to downstream LLMs, alongside prohibitive computational costs. To resolve this, we propose MemRec, a framework that architectural...