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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 ...
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 ...