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Customized Conversational Recommender Systems
Shuokai Li, Yongchun Zhu, Ruobing Xie +5
Conversational recommender systems (CRS) aim to capture user's current intentions and provide recommendations through real-time multi-turn conversational interactions. As a human-m…
Detect Professional Malicious User with Metric Learning in Recommender Systems
Yuanbo Xu, Yongjian Yang, En Wang +2
In e-commerce, online retailers are usually suffering from professional malicious users (PMUs), who utilize negative reviews and low ratings to their consumed products on purpose t…
Personalized Prompt for Sequential Recommendation
Yiqing Wu, Ruobing Xie, Yongchun Zhu +4
Pre-training models have shown their power in sequential recommendation. Recently, prompt has been widely explored and verified for tuning in NLP pre-training, which could help to…
Selective Fairness in Recommendation via Prompts
Yiqing Wu, Ruobing Xie, Yongchun Zhu +5
Recommendation fairness has attracted great attention recently. In real-world systems, users usually have multiple sensitive attributes (e.g. age, gender, and occupation), and user…