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http://dx.doi.org/10.9708/jksci.2011.16.11.017

GGenre Pattern based User Clustering for Performance Improvement of Collaborative Filtering System  

Choi, Ja-Hyun (Department of Computer.Information Engineering, Inha University)
Ha, In-Ay (Department of Computer.Information Engineering, Inha University)
Hong, Myung-Duk (Department of Computer.Information Engineering, Inha University)
Jo, Geun-Sik (School of Computer.Information Engineering, Inha University)
Publication Information
Journal of the Korea Society of Computer and Information / v.16, no.11, 2011 , pp. 17-24 More about this Journal
Abstract
Collaborative filtering system is the clustering about user is built and then based on that clustering results will recommend the preferred item to the user. However, building user clustering is time consuming and also once the users evaluate and give feedback about the film then rebuilding the system is not simple. In this paper, genre pattern of movie recommendation systems is being used and in order to simplify and reduce time of rebuilding user clustering. A Frequent pattern networks is used and then extracts user preference genre patterns and through that extracted patterns user clustering will be built. Through built the clustering for all neighboring users to collaborative filtering is applied and then recommends movies to the user. When receiving user information feedback, traditional collaborative filtering is to rebuild the clustering for all neighbouring users to research and do the clustering. However by using frequent pattern Networks, through user clustering based on genre pattern, collaborative filtering is applied and when rebuilding user clustering inquiry limited by search time can be reduced. After receiving user information feedback through proposed user clustering based on genre pattern, the time that need to spent on re-establishing user clustering can be reduced and also enable the possibility of traditional collaborative filtering systems and recommendation of a similar performance.
Keywords
Collaborative Filtering; Frequent Pattern Networks; User Clustering;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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