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:2608. 15949v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have enabled their use as conversational recommender systems (CRS), demonstrating strong recommendation accuracy and natural dialogue.
By Cedar Site Bai, Duanshun Li, Zhenyu Liao, Sheikh Sarwar, Huiyuan Chen, Yuan Chen, Changhe Yuan, Haiyang Zhang, Qilin Qi
FacetCRS is a conversational recommender system that tackles the filter‑bubble problem by learning multi‑faceted user preferences—entity, word, context, and review facets—through natural language interactions. The framework adaptively models these preference facets and incorporates external knowledge to provide diverse recommendations. Experiments on two benchmark datasets show that FacetCRS outperforms existing methods in reducing filter bubbles and improving recommendation quality.
By Yongsen Zheng, Ziliang Chen, Jinghui Qin, Liang Lin
arXiv:2607. 00010v1 Announce Type: cross Abstract: Conversational recommender systems (CRSs) are a core component of next-generation intelligent recommender systems because they enable users to actively elicit preferences, clarify intentions, and adapt recommendations in real time.
By Nipun B Nair, Tongtong Wu, Weiqing Wang
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:2603.04191v2 Announce Type: replace
Abstract: Large Language Models (LLMs) are increasingly serving as personal assistants, where users may share individual preferences over extended interactio...
By Qianyun Guo, Yibo Li, Yue Liu, Bryan Hooi