arXiv AI By Zichun Jin, Zihan Zhou, Yinan Liu, Bin Wang, Xiaochun Yang

Adapting Knowledge Graphs for Behavior Denoising in Sequential Recommendation

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arXiv Machine Learning
Jul 14

RecRec: Recursive Refinement for Sequential Recommendation

arXiv:2607. 10541v1 Announce Type: cross Abstract: Sequential recommender systems typically infer user preferences through single-pass encoding of interaction histories without iterative refinement, relying on increasingly deep architectures to capture complex patterns.

By Pervez Shaik, Prosenjit Biswas, Abhinav Thorat, Ravi Kolla, Niranjan Pedanekar
arXiv AI
Sep 7

Continual Graph Memory for Adaptive Recommendation under Intent Drift

The paper introduces CGM-Rec, a continual graph memory framework designed for adaptive recommendation in the presence of intent drift. CGM-Rec treats the knowledge graph as writable memory, comprising a conservative Semantic Graph Memory for stable relational knowledge and a fast-reactive Episodic Lesson Memory for recent outcomes and corrective hints. Experiments show that, with frozen model parameters and one-pass reranking, CGM-Rec outperforms neural and LLM-based baselines across multiple recommendation settings, achieving significant gains such as a 29.58% improvement in HR@1 over the strongest LLM baseline on Bundle.

By Hao Nguyen Ngoc, Tung Nguyen, Nguyen Thi Hanh, Hoang Thai Dinh, Nguyen Xuan Tung