Özet:

Recommender systems are automated tools that suggest appropriate products/services to individual users based on their preferences in the past and characteristics without requiring any personal effort. In these systems, collaborative filtering algorithms are the most utilized approaches to produce individual predictions or a ranked list of preferable items for users. Although such algorithms' efficiency is generally assessed with the accuracy of provided recommendations, beyond-accuracy evaluations such as item catalog coverage are also considered critical factors in qualified recommendations. However, many recent studies demonstrate that these algorithms tend to feature certain items than others in the produced ranked lists because of their specific properties (e.g., popularity). In this study, we scrutinize item profiles with a different point of view, the degrees of being liked, and investigate whether there is any bias of collaborative filtering algorithms towards highly-liked items or not. To this end, we adopt nine prominent collaborative filtering algorithms in three different categories and perform various experiments on two real-world datasets. The experimental results demonstrate that almost all algorithms are strongly biased towards highly-liked items, and matrix factorization based algorithms such as SVD and SVD++ are more successful than others in producing high-quality recommendations.

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