5 papers
When and How to Ask: Dynamic Preference Elicitation Strategies for Conversational Recommendation
Feng Xia, Shuo Zhang, Xi Wang
Conversational Recommender Systems (CRSs) are interactive systems that use multi-turn natural language dialogue to understand evolving user preferences and provide personalized rec…
Interplay: Training Independent Simulators for Reference-Free Conversational Recommendation
Jerome Ramos, Feng Xia, Xi Wang +4
Training conversational recommender systems (CRS) requires extensive dialogue data, which is challenging to collect at scale. To address this, researchers have used simulated user-…
Explaining Synergistic Effects in Social Recommendations
Yicong Li, Shan Jin, Qi Liu +6
In social recommenders, the inherent nonlinearity and opacity of synergistic effects across multiple social networks hinders users from understanding how diverse information is lev…
ORCA: Mitigating Over-Reliance for Multi-Task Dwell Time Prediction with Causal Decoupling
Huishi Luo, Fuzhen Zhuang, Yongchun Zhu +6
Dwell time (DT) is a critical post-click metric for evaluating user preference in recommender systems, complementing the traditional click-through rate (CTR). Although multi-task l…
Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User Feedback
Guipeng Xv, Xinyu Li, Ruobing Xie +5
Multi-modal recommender systems (MRSs) are pivotal in diverse online web platforms and have garnered considerable attention in recent years. However, previous studies overlook the…