MemRec: Collaborative Memory-Augmented Agentic Recommender System (2026.acl-long)
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Weixin Chen, Yuhan Zhao, Jingyuan Huang, Zihe Ye, Mingxuan Ju, Tong Zhao, Neil Shah, Li Chen, Yongfeng Zhang
| Challenge: | Existing recommender systems rely on semantic user and item memories to make predictions, but these memories are kept in isolation. |
| Approach: | They propose a framework that architecturally decouples memory management from reasoning to decouple memory management and reasoning from the user and item memories. |
| Outcome: | The proposed framework decouples memory management from reasoning and achieves state-of-the-art performance on four benchmarks. |
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