As one of the most pervasive applications of machine learning, recommender systems are playing an important role on assisting human decision making. The satisfaction of users and …
As information filtering services, recommender systems have extremely enriched our daily life by providing personalized suggestions and facilitating people in decision-making, which …
Prior research on exposure fairness in the context of recommender systems has focused mostly on disparities in the exposure of individual or groups of items to individual users of …
Recommender systems (RSs) aim at helping users to effectively retrieve items of their interests from a large catalogue. For a quite long time, researchers and practitioners have …
Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making …
Achieving fairness over different user groups in recommender systems is an important problem. The majority of existing works achieve fairness through constrained optimization …
Y Wu, J Cao, G Xu - ACM Transactions on Knowledge Discovery from …, 2023 - dl.acm.org
With the wide application of recommender systems, the potential impacts of recommender systems on customers, item providers and other parties have attracted increasing attention …
User modeling techniques profile users' latent characteristics (eg, preference) from their observed behaviors, and play a crucial role in decision-making. Unfortunately, traditional …
Recent advancements in foundation models such as large language models (LLM) have propelled them to the forefront of recommender systems (RS). Moreover, fairness in RS is …