As its availability and generality in online services, implicit feedback is more commonly used
in recommender systems. However, implicit feedback usually presents noisy samples in real …

The explainability of recommender systems has attracted significant attention in academia
and industry. Many efforts have been made for explainable recommendations, yet …

Collaborative Filtering~(CF) typically suffers from the significant challenge of popularity bias
due to the uneven distribution of items in real-world datasets. This bias leads to a significant …

As recommender systems become pervasive in various scenarios, cross-domain
recommenders (CDR) are proposed to enhance the performance of one target domain with …

LLM-based agents have gained considerable attention for their decision-making skills and
ability to handle complex tasks. Recognizing the current gap in leveraging agent capabilities …

Providing reasonable explanations for a specific suggestion given by the recommender can
help users trust the system more. As logic rule-based inference is concise, transparent, and …

From e-commerce to music and news, recommender systems are tailored to specific
scenarios. While researching generic models applicable to various scenarios is crucial …

Multimedia recommendation, which incorporates various modalities (eg, images, texts, etc.)
into user or item representation to improve recommendation quality, has received …

Learning user preferences from implicit feedback is one of the core challenges in
recommendation. The difficulty lies in the potential noise within implicit feedback. Therefore …