Abstract
Collaborative filtering has been successfully implemented in many commercial recommender systems. These systems recommend items favored by other users with similar preference history to the current user. As finding similar users is critical to the performance of the system, various techniques have been suggested to develop similarity measures. However, there are still much to be improved, because existing similarity measures simply utilize additional heuristic information and seldom reflect the global rating behaviors on items. This paper aims to improve the previous similarity measures by employing the information entropy of user ratings so that the user’s global rating behavior on items can be reflected. The efficiency of the proposed method is examined through extensive experiments to demonstrate its superior performance over the previous similarity measures especially in small-scaled and sparse datasets.
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Lee, S. Using entropy for similarity measures in collaborative filtering. J Ambient Intell Human Comput 11, 363–374 (2020). https://doi.org/10.1007/s12652-019-01226-0
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DOI: https://doi.org/10.1007/s12652-019-01226-0