Recommender systems (RS) are effective tools for mitigating information overload and have
seen extensive applications across various domains. However, the single focus on utility …

Fair re-ranking aims to redistribute ranking slots among items more equitably to ensure
responsibility and ethics. The exploration of redistribution problems has a long history in …

The recent growing concerns surrounding the pervasive adoption of generative AI can be
traced back to the long-standing influence of AI algorithms that have predominantly served …

User-side group fairness is crucial for modern recommender systems, alleviating
performance disparities among user groups defined by sensitive attributes like gender, race …

Recommender systems learn personalized user preferences from user feedback like clicks.
However, user feedback is usually biased towards partially observed interests, leaving many …

Time-aware recommendation has been widely studied for modeling the user dynamic
preference and a lot of models have been proposed. However, these models often overlook …

Multimodal recommender systems have acquired applications in broad web scenarios such
as e-commerce businesses and short-video platforms. Existing multimodal recommendation …

At present, most recommender systems involve two stakeholders, providers and customers.
Apart from maximizing the recommendation accuracy, the fairness issue for both sides …

Popularity bias is commonly observed in recommendation results. Directly fitting biased data
can significantly affect the quality of recommendations for long-tail items. To eliminate …

Achieving fairness among different individuals or groups is an essential task for industrial
recommender systems. Due to the group's personalized selection tendencies and the non …