128 citations · 927 across the 99 of their papers we have counts for
7 papers · 1 filter
Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation
Xiaoyu Zhang, Xin Xin, Dongdong Li +5
Conversational recommender systems (CRSs) often utilize external knowledge graphs (KGs) to introduce rich semantic information and recommend relevant items through natural language…
Contrastive Learning Reduces Hallucination in Conversations
Weiwei Sun, Zhengliang Shi, Shen Gao +3
Pre-trained language models (LMs) store knowledge in their parameters and can generate informative responses when used in conversational systems. However, LMs suffer from the probl…
On the User Behavior Leakage from Recommender System Exposure
Xin Xin, Jiyuan Yang, Hanbing Wang +6
Modern recommender systems are trained to predict users potential future interactions from users historical behavior data. During the interaction process, despite the data coming f…
Debiasing Learning for Membership Inference Attacks Against Recommender Systems
Zihan Wang, Na Huang, Fei Sun +5
Learned recommender systems may inadvertently leak information about their training data, leading to privacy violations. We investigate privacy threats faced by recommender systems…
Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective
Xin Xin, Tiago Pimentel, Alexandros Karatzoglou +3
Modern recommender systems aim to improve user experience. As reinforcement learning (RL) naturally fits this objective -- maximizing an user's reward per session -- it has become…
Paying More Attention to Self-attention: Improving Pre-trained Language Models via Attention Guiding
Shanshan Wang, Zhumin Chen, Zhaochun Ren +3
Pre-trained language models (PLM) have demonstrated their effectiveness for a broad range of information retrieval and natural language processing tasks. As the core part of PLM, m…