A comprehensive survey on trustworthy recommender systems
As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …
people make appropriate decisions in an effective and efficient way, by providing …
Fairness in recommendation: A survey
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 …
playing an important role on assisting human decision making. The satisfaction of users and …
[HTML][HTML] A survey on fairness-aware recommender systems
As information filtering services, recommender systems have extremely enriched our daily
life by providing personalized suggestions and facilitating people in decision-making, which …
life by providing personalized suggestions and facilitating people in decision-making, which …
Joint multisided exposure fairness for recommendation
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 …
mostly on disparities in the exposure of individual or groups of items to individual users of …
Trustworthy recommender systems
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 …
interests from a large catalogue. For a quite long time, researchers and practitioners have …
A survey on trustworthy recommender systems
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 …
deployed in almost every corner of the web and facilitate the human decision-making …
Fairness-aware federated matrix factorization
Achieving fairness over different user groups in recommender systems is an important
problem. The majority of existing works achieve fairness through constrained optimization …
problem. The majority of existing works achieve fairness through constrained optimization …
Fairness in recommender systems: evaluation approaches and assurance strategies
With the wide application of recommender systems, the potential impacts of recommender
systems on customers, item providers and other parties have attracted increasing attention …
systems on customers, item providers and other parties have attracted increasing attention …
Fairlisa: Fair user modeling with limited sensitive attributes information
User modeling techniques profile users' latent characteristics (eg, preference) from their
observed behaviors, and play a crucial role in decision-making. Unfortunately, traditional …
observed behaviors, and play a crucial role in decision-making. Unfortunately, traditional …
Up5: Unbiased foundation model for fairness-aware recommendation
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 …
propelled them to the forefront of recommender systems (RS). Moreover, fairness in RS is …