Movie Recommendation Using Co-Clustering by Infinite Relational Models

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 ex-plainable relations among users and movies. We suggest various usages of IRM-based co-clustering, espcially, for in-cremental and dynamic recommender systems. Key Words : Recommender systems, Group preferences, Relational Analysis, Movie Recommendation, Infinite Relational Model, Co-Clustering

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