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http://dx.doi.org/10.5391/JKIIS.2014.24.4.443

Movie Recommendation Using Co-Clustering by Infinite Relational Models  

Kim, Byoung-Hee (School of Computer Science & Engineering, Seoul National University)
Zhang, Byoung-Tak (School of Computer Science & Engineering, Seoul National University)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.24, no.4, 2014 , pp. 443-449 More about this Journal
Abstract
Preferences of users on movies are observables of various factors that are related with user attributes and movie features. For movie recommendation, analysis methods for relation among users, movies, and preference patterns are mandatory. As a relational analysis tool, we focus on the Infinite Relational Model (IRM) which was introduced as a tool for multiple concept search. We show that IRM-based co-clustering on preference patterns and movie descriptors can be used as the first tool for movie recommender methods, especially content-based filtering approaches. By introducing a set of well-defined tag sets for movies and doing three-way co-clustering on a movie-rating matrix and a movie-tag matrix, we discovered various explainable relations among users and movies. We suggest various usages of IRM-based co-clustering, espcially, for incremental and dynamic recommender systems.
Keywords
Recommender systems; Group preferences; Relational Analysis; Movie Recommendation; Infinite Relational Model; Co-Clustering;
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