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 …

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 …

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 …

Most real-world recommender systems are deployed in a commercial context or designed to
represent a value-adding service, eg, on shopping or Social Web platforms, and typical …

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 …

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 …

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 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 …

Recommendation algorithms are known to suffer from popularity bias; a few popular items
are recommended frequently while the majority of other items are ignored. These …