arXiv:2607. 18609v1 Announce Type: cross Abstract: The Matthew effect is a big challenge in Recommender Systems (RSs), where popular items tend to receive increasing attention, while less popular ones are often overlooked, perpetuating existing disparities.
By Yongsen Zheng, Ruilin Xu, Guohua Wang, Liang Lin, Kwok-Yan Lam
arXiv:2607. 17461v1 Announce Type: cross Abstract: The Matthew effect is a notorious issue in Recommender Systems (RSs), \emph{i.
By Yongsen Zheng, Ruilin Xu, Ziliang Chen, Guohua Wang, Mingjie Qian, Jinghui Qin, Liang Lin
Re2A is a new framework for situated conversational recommendation that models user interactions within shared physical environments. It introduces rubric-based preference reasoning to explicitly capture user preferences from dialogue history and scene context, and a preference-conditioned optimization to align generated responses with both user satisfaction and situational consistency. Experiments on two SCR datasets show that Re2A outperforms existing methods, providing more precise and context-aware recommendations.
By Dongding Lin, Jian Wang, Xiaoyan Zhao, Wenjie Li
arXiv:2606. 03866v1 Announce Type: cross Abstract: Scaling recommender systems via large language models (LLMs) has become a prominent trend in the industry.
By Yuecheng Li, Zeyu Song, Jing Yao, Chi Lu, Peng Jiang, Kun Gai
arXiv:2506. 07449v2 Announce Type: replace-cross Abstract: Recent advances in Large Language Models (LLMs) have driven their adoption in recommender systems through Retrieval-Augmented Generation (RAG) frameworks.
By Vahid Azizi, Fatemeh Koochaki
arXiv:2608. 06632v1 Announce Type: new Abstract: Industrial recommendation systems predominantly adopt a passive ranking paradigm that infers user preferences from implicit behavioral signals (e.
By Ziyun Xu, Bosen Ding, Yue Zhang, Ji Qi, Qingyuan Song, Jizhou Huang, Liwei Wang, Jefferey Santelli, Yue Weng, Qichao Que, Zhenheng Yang, Junfeng Pan, Linhong Zhu