Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Movie Recommendation Using Co-Clustering by Infinite Relational Models

Infinite Relational Model 기반 Co-Clustering을 이용한 영화 추천

  • Kim, Byoung-Hee (School of Computer Science & Engineering, Seoul National University) ;
  • Zhang, Byoung-Tak (School of Computer Science & Engineering, Seoul National University)
  • Received : 2014.03.09
  • Accepted : 2014.06.02
  • Published : 2014.08.25
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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.

사람의 영화에 대한 선호도에는 개인의 특성과 영화의 속성을 기반으로 하는 다양한 요인이 연관되어 있다. 영화 추천을 위한 사용자-영화-선호도 연관 관계의 분석 기법으로서, 다중 개념 탐색 기법의 특성을 지닌 infinite relational model (IRM)의 활용 가능성을 확인하고, 이를 기초로 영화 선호 유형에 따른 사용자-영화 군집을 탐색한다. 별점으로 표현되는 명시적인 선호도 데이터에 영화 컨텐츠 관련 메타데이터를 추가하여 학습 데이터를 구성하고, 이에 IRM을 적용하여 공군집화(co-clustering)를 수행한 결과, 해석 가능한 다양한 명시적 연관 관계를 발견하였다. 공군집화 결과를 기초로 개인화 추천에서의 다양한 활용 방안을 논의한다.

Keywords

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