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 help people find relevant content in a personalized way. One main
promise of such systems is that they are able to increase the visibility of items in the long tail …

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 …

Traditionally, especially in academic research in recommender systems, the focus has been
solely on the satisfaction of the end-user. While user satisfaction has, indeed, been …

Recommender Systems are decision support tools that adopt advanced algorithms in order
to help users to find less-explored items that can be interesting for them. While …

While recommender systems are highly successful at helping users find relevant information
online, they may also exhibit a certain undesired bias of mostly promoting only already …

Despite their proven various benefits, Recommender Systems can cause or amplify certain
undesired effects. In this paper, we focus on Popularity Bias, ie, the tendency of a …

Popularity bias in recommendation lists refers to over-representation of popular content and
is a challenge for many recommendation algorithms. Previous research has suggested …

Popularity bias is a well-known phenomenon in recommender systems: popular items are
recommended even more frequently than their popularity would warrant, amplifying long-tail …