Fuzzy Clustering with Genre Preference for Collaborative Filtering (협력필터링을 위한 장르 선호도에 대한 퍼지 클러스터링)

Soojung Lee (이수정)

doi:10.9708/jksci.2020.25.05.099

Fuzzy Clustering with Genre Preference for Collaborative Filtering

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2020, 25(5), pp.99-106
DOI : 10.9708/jksci.2020.25.05.099
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : March 31, 2020
Accepted : May 12, 2020
Published : May 29, 2020

Soojung Lee ¹

¹경인교육대학교

Accredited

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

Citation status

* References for papers published after 2023 are currently being built.

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KJCKorea
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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Fuzzy Clustering with Genre Preference for Collaborative Filtering

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Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Fuzzy Clustering with Genre Preference for Collaborative Filtering

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.