Collaborative filtering for predicting users' potential preferences

Authors:
Kenta Oku;Ta Son Tung;Fumio Hattori
Affiliations:
College of Information Science and Engineering, Ritsumeikan University, Shiga, Japan;College of Information Science and Engineering, Ritsumeikan University, Shiga, Japan;College of Information Science and Engineering, Ritsumeikan University, Shiga, Japan
Venue:
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part IV
Year:
2011

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Abstract

Our goal is to establish a method for predicting users' potential preference. We define a potential preference as a preference for the unknown genres for the target user. However, it is difficult to predict the potential preference by conventional recommender systems because there is little or no preference data (i.e. ratings for items) for the users' unknown genres. Accordingly, we propose a collaborative filtering for predicting the users' potential preference by their ratings in their known genres. Experimental results using MovieLens data sets showed that the genre relevance influences the prediction accuracy of the potential preference in the unknown genres.