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Treating adverse effects of blockbuster bias on beyond-accuracy quality of personalized recommendations
(Elsevier, 2022)
Collaborative filtering recommendation algorithms are vulnerable against the popularity bias, including the most popular items repeatedly into the produced ranked lists. However, the research on popularity bias focuses ...
Robustness of privacy-preserving collaborative recommenders against popularity bias problem
(PeerJ, Temmuz, 20)
Recommender systems have become increasingly important in today’s digital age, but they are not without their challenges. One of the most significant challenges is that users are not always willing to share their preferences ...
Effects of Neighborhood-based Collaborative Filtering Parameters on Their Blockbuster Bias Performances
(Ağustos, 2)
Collaborative filtering algorithms are efficient tools for providing recommendations with reasonable accuracy performances to individuals. However, the previous research has realized that these algorithms are undesirably ...
Blockbuster: A New Perspective on Popularity-bias in Recommender Systems
(IEEE, Ekim, 2021)
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 attention, also referred to as the ...