Exploring potential biases towards blockbuster items in ranking-based recommendations
E Yalcin - Data Mining and Knowledge Discovery, 2022 - Springer
Popularity bias is defined as the intrinsic tendency of recommendation algorithms to feature
popular items more than unpopular ones in the ranked lists lists they produced. When …
popular items more than unpopular ones in the ranked lists lists they produced. When …
The unfairness of popularity bias in recommendation
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 …
frequently rated) items get a lot of exposure while less popular ones are under-represented …
[HTML][HTML] Treating adverse effects of blockbuster bias on beyond-accuracy quality of personalized recommendations
Collaborative filtering recommendation algorithms are vulnerable against the popularity
bias, including the most popular items repeatedly into the produced ranked lists. However …
bias, including the most popular items repeatedly into the produced ranked lists. However …
The unfairness of popularity bias in book recommendation
Recent studies have shown that recommendation systems commonly suffer from popularity
bias. Popularity bias refers to the problem that popular items (ie, frequently rated items) are …
bias. Popularity bias refers to the problem that popular items (ie, frequently rated items) are …
Novel approaches to measuring the popularity inclination of users for the popularity bias problem
An efficient approach to handling the well-known popularity bias in recommendations is
treating this issue by considering users' actual propensities on item popularity. This way, the …
treating this issue by considering users' actual propensities on item popularity. This way, the …
Popularity bias in recommender systems-a review
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 …
them more accurate to making them fairer and diverse, thus catering to the set of less …
An adaptive boosting technique to mitigate popularity bias in recommender system
The observed ratings in most recommender systems are subjected to popularity bias and
are thus not randomly missing. Due to this, only a few popular items are recommended, and …
are thus not randomly missing. Due to this, only a few popular items are recommended, and …
Blockbuster: A new perspective on popularity-bias in recommender systems
E Yalcin - 2021 6th International Conference on Computer …, 2021 - ieeexplore.ieee.org
Collaborative filtering algorithms unwittingly produce ranked lists where a few popular items
are recommended too frequently while the remaining vast amount of items get not deserved …
are recommended too frequently while the remaining vast amount of items get not deserved …
Multi-sided exposure bias in recommendation
H Abdollahpouri, M Mansoury - arXiv preprint arXiv:2006.15772, 2020 - arxiv.org
Academic research in recommender systems has been greatly focusing on the accuracy-
related measures of recommendations. Even when non-accuracy measures such as …
related measures of recommendations. Even when non-accuracy measures such as …
Addressing the multistakeholder impact of popularity bias in recommendation through calibration
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 …
recommended even more frequently than their popularity would warrant, amplifying long-tail …