activity
20212024
most citedTowards Explainable Conversational Recommender Systems

35 citations · 116 across the 23 of their papers we have counts for

collaborators

26 papers

cs.IR202421 cited

Towards Empathetic Conversational Recommender Systems

Xiaoyu Zhang, Ruobing Xie, Yougang Lyu +7

Conversational recommender systems (CRSs) are able to elicit user preferences through multi-turn dialogues. They typically incorporate external knowledge and pre-trained language m…

cs.CL2024

Enhancing Multi-hop Reasoning through Knowledge Erasure in Large Language Model Editing

Mengqi Zhang, Bowen Fang, Qiang Liu +4

Large language models (LLMs) face challenges with internal knowledge inaccuracies and outdated information. Knowledge editing has emerged as a pivotal approach to mitigate these is…

cs.MA2024

Beyond Local Views: Global State Inference with Diffusion Models for Cooperative Multi-Agent Reinforcement Learning

Zhiwei Xu, Hangyu Mao, Nianmin Zhang +8

In partially observable multi-agent systems, agents typically only have access to local observations. This severely hinders their ability to make precise decisions, particularly du…

cs.IR2024

ExcluIR: Exclusionary Neural Information Retrieval

Wenhao Zhang, Mengqi Zhang, Shiguang Wu +5

Exclusion is an important and universal linguistic skill that humans use to express what they do not want. However, in information retrieval community, there is little research on…

cs.IR2024

Generative Retrieval as Multi-Vector Dense Retrieval

Shiguang Wu, Wenda Wei, Mengqi Zhang +5

Generative retrieval generates identifiers of relevant documents in an end-to-end manner using a sequence-to-sequence architecture for a given query. The relation between generativ…

cs.IR20242 cited

Uncovering Selective State Space Model's Capabilities in Lifelong Sequential Recommendation

Jiyuan Yang, Yuanzi Li, Jingyu Zhao +8

Sequential Recommenders have been widely applied in various online services, aiming to model users' dynamic interests from their sequential interactions. With users increasingly en…