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http://dx.doi.org/10.9708/jksci.2020.25.05.099

Fuzzy Clustering with Genre Preference for Collaborative Filtering  

Lee, Soojung (Dept. of Computer Education, Gyeongin National University of Education)
Publication Information
Journal of the Korea Society of Computer and Information / v.25, no.5, 2020 , pp. 99-106 More about this Journal
Abstract
The scalability problem inherent in collaborative filtering-based recommender systems has been an issue in related studies during past decades. Clustering is a well-known technique for handling this problem, but has not been actively studied due to its low performance. This paper adopts a clustering method to overcome the scalability problem, inherent drawback of collaborative filtering systems. Furthermore, in order to handle performance degradation caused by applying clustering into collaborative filtering, we take two strategies into account. First, we use fuzzy clustering and secondly, we propose and apply a similarity estimation method based on user preference for movie genres. The proposed method of this study is evaluated through experiments and compared with several previous relevant methods in terms of major performance metrics. Experimental results show that the proposed demonstrated superior performance in prediction and rank accuracies and comparable performance to the best method in our experiments in recommendation accuracy.
Keywords
Collaborative Filtering; Recommender System; Similarity Measure; Scalability Problem; Fuzzy Clustering;
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Times Cited By KSCI : 2  (Citation Analysis)
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