The paper introduces Evo-Rec, a three‑stage framework that improves generative recommendation by learning better reasoning traces for Semantic ID (SID) generation. It first aligns SIDs with textual and behavioral contexts, then selects candidate reasoning traces that improve ground‑truth item prediction, and finally refines the reasoning policy via reinforcement learning with catalog‑constrained generation and ranking‑aware feedback. Experiments on Amazon Review datasets show Evo‑Rec consistently outperforms existing discriminative, generative, and reasoning‑enhanced recommenders across all metrics.
By Mengdan Zhu, Yufan Zhao, Sophie Di, Yao Zhao, Tao Di, Yulan Yan, Sridhar Iyer, Liang Zhao
arXiv:2608. 19735v1 Announce Type: new Abstract: We introduce RecPFN, a prior-fitted network that brings in-context learning to sequential recommendation.
By En Zhi Tan, Jia Xiang Lim, Bryan Lijie Chew, Tze Minh Ng, Benjamin Yan Han Yap
ReMem is a new recommendation agent framework that rethinks perception and memory for long-context recommendation tasks. It replaces raw HTML parsing with OCR-based multimodal perception from screenshots, extracting structured information in a platform-agnostic way. The framework also introduces a chunk-wise sequential memory update strategy and a multi-memory GRPO variant to efficiently model evolving user preferences over arbitrarily long interaction histories, achieving a 5.16% average improvement over state-of-the-art baselines on three recommendation agent tasks.
By Haohao Qu, Yongcheng Jing, Chun Hin Chan, Shanru Lin, Wenqi Fan, Dacheng Tao
arXiv:2608.21243v1 Announce Type: cross
Abstract: Sequential recommendation predicts the next item from a user's interaction history, but not every interaction is equally informative. Real logs combi...
By Zichun Jin, Zihan Zhou, Yinan Liu, Bin Wang, Xiaochun Yang
The paper introduces Evo-Rec, a three‑stage framework that improves generative recommendation by learning better reasoning traces for Semantic ID (SID) generation. First, it aligns SIDs with textual and behavioral contexts; second, it samples and selects reasoning traces that improve ground‑truth item prediction via supervised fine‑tuning; third, it refines the reasoning policy with reinforcement learning using catalog‑constrained generation and ranking‑aware feedback. Experiments on three Amazon Review datasets show Evo‑Rec consistently outperforms discriminative, generative, and other reasoning‑enhanced recommenders across all metrics.
arXiv:2607. 23762v1 Announce Type: cross Abstract: Conventional recommenders capture users' preferences by optimizing observed user-item relations, whereas continuous generative recommendation additionally learns the trajectory of synthesizing a target item.
By Dengzhao Fang, Jingtong Gao, Yu Li, Xiangyu Zhao, Yi Chang
arXiv:2606. 11023v1 Announce Type: cross Abstract: Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior.
By Yifan Li, Jiahong Liu, Xinni Zhang, Hao Chen, Yankai Chen, Wenhao Yu, Jianting Chen, Irwin King
arXiv:2604. 05379v2 Announce Type: replace-cross Abstract: The sequential recommendation (SR) task aims to predict the next item based on users' historical interaction sequences.
By Xing Tang, Ziqiang Cui, Jingyang Bin, Xiaokun Zhang, Fuyuan Lyu, Jingyan Jiang, Dugang Liu, Chen Ma, Xiuqiang He
arXiv:2607. 24845v1 Announce Type: cross Abstract: Large language models (LLMs) have been applied to sequential recommendation by formulating it as a natural language task.
By Harshini Kavuru, Dwipam Katariya, Giri Iyengar, Pranab Mohanty, Kalanand Mishra, Kalanand Mishra
arXiv:2603. 21613v2 Announce Type: replace-cross Abstract: Recommender agents built on Large Language Models offer a promising paradigm for personalized recommendation.
By Tianyi Li, Zixuan Wang, Guidong Lei, Xiaodong Li, Hui Li
CRAMER is a framework that enables sequential recommendation models to adapt instantly to user requests by treating natural‑language requests as control signals and applying request‑aware masking to frozen backbone parameters. This approach avoids costly retraining or large language model inference, achieving minimal overhead. Experiments on large‑scale benchmarks demonstrate that CRAMER outperforms four state‑of‑the‑art request‑aware baselines while offering enhanced controllability and cross‑domain adaptability.
By Zhiyuan Julian Su, Naihe Feng, Zhen Luther Qin, Ga Wu
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