Recommender system usually faces popularity bias issues: from the data perspective, items
exhibit uneven (usually long-tail) distribution on the interaction frequency; from the method …

Recommendation, information retrieval, and other information access systems pose unique
challenges for investigating and applying the fairness and non-discrimination concepts that …

Recommendation and ranking systems are known to suffer from popularity bias; the
tendency of the algorithm to favor a few popular items while under-representing the majority …

With the advancement in recommendation techniques, focus is diverted from just making
them more accurate to making them fairer and diverse, thus catering to the set of less …

The popularity bias problem is one of the most prominent challenges of recommender
systems, ie, while a few heavily rated items receive much attention in presented …

Recommender systems are poised at the interface between stakeholders: for example, job
applicants and employers in the case of recommendations of employment listings, or artists …

Multistakeholder recommendation is the term applied when a recommender system is
designed, implemented and/or evaluated taking into account the perspectives of multiple …

Session-based recommendation is misleading by popularity bias and always favors short-
head items with more popularity. This paper studies a new causal-based framework …

Most recommender systems evaluate model performance offline through either: 1) normal
biased test on factual interactions; or 2) debiased test with records from the randomized …

Graph convolutional network (GCN) has been extensively applied to recommender systems
(RS) and achieved significant performance improvements through iteratively aggregating …