Recommender systems can strongly influence which information we see online, eg, on
social media, and thus impact our beliefs, decisions, and actions. At the same time, these …

Recommender systems provide personalized information access to users of Internet
services from social networks to e-commerce to media and entertainment. As is appropriate …

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 …

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 …

The general aim of the recommender system is to provide personalized suggestions to
users, which is opposed to suggesting popular items. However, the normal training …

As a highly data-driven application, recommender systems could be affected by data bias,
resulting in unfair results for different data groups, which could be a reason that affects the …

Recently, there has been a rising awareness that when machine learning (ML) algorithms
are used to automate choices, they may treat/affect individuals unfairly, with legal, ethical, or …

Recommender systems are gaining increasing and critical impacts on human and society
since a growing number of users use them for information seeking and decision making …

Recommender systems are known to suffer from the popularity bias problem: popular (ie
frequently rated) items get a lot of exposure while less popular ones are under-represented …

Recommender Systems have shown to be an effective way to alleviate the over-choice
problem and provide accurate and tailored recommendations. However, the impressive …