Abstract
Existing recommender systems suffer from a popularity bias problem. Popular items are always recommended to users regardless whether they are related to users’ preferences. In this paper, we propose an opinion-based collaborative filtering by introducing weighting functions to adjust the influence of popular items. Based on conventional user-based collaborative filtering, the weighting functions are used in measuring users’ similarities so that the effect of popular items is decreased with similar opinions and increased with dissimilar ones. Experiments verify the effectiveness of our proposed approach.
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Zhao, X., Niu, Z., Chen, W. (2013). Opinion-Based Collaborative Filtering to Solve Popularity Bias in Recommender Systems. In: Decker, H., Lhotská, L., Link, S., Basl, J., Tjoa, A.M. (eds) Database and Expert Systems Applications. DEXA 2013. Lecture Notes in Computer Science, vol 8056. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40173-2_35
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DOI: https://doi.org/10.1007/978-3-642-40173-2_35
Publisher Name: Springer, Berlin, Heidelberg
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