Akademik Veri Yönetim Sistemi
DATA MINING AND KNOWLEDGE DISCOVERY, cilt.36, sa.6, ss.2033-2073, 2022 (SCI-Expanded)
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 investigating the adverse effects of popularity bias, the literature has usually focused on the most frequently rated items only. However, an item's popularity does not always indicate that it is highly-liked by individuals; in fact, the degree of liking may even introduce biases that are more extreme than the famous popularity bias in terms of beyond-accuracy evaluations. Therefore, in the present study, we attempt to consider items that are both popular and highly-liked, which we refer to as blockbuster items, and to investigate whether the recommendation algorithms impose a considerable bias in favor of the blockbuster items in their ranking-based recommendations. To this end, we first present a practical formulation that measures the degree of the blockbuster level of the items by combining their liking-degree and popularity effectively. Then, based on this formulation, we perform a comprehensive set of experiments with ten different algorithms on five datasets with different characteristics to explore the potential biases towards blockbuster items in recommendations. The experimental outcomes demonstrate that most recommenders propagate an undesirable bias in their recommendations towards the blockbuster items, and such a bias is, in fact, not caused by the item popularity. Moreover, the observed biases to blockbuster items are more harmful and persistent than those to popular ones in terms of beyond-accuracy aspects such as diversity, catalog coverage, and novelty. The obtained results also suggest that conventional popularity-debiasing strategies are not so talented in treating the adverse effects of the observed blockbuster bias in recommendations.