Context-Aware SVM for Context-Dependent Information Recommendation

Authors:
Kenta Oku;Shinsuke Nakajima;Jun Miyazaki;Shunsuke Uemura
Affiliations:
Nara Institute of Science and Technology, Japan;Nara Institute of Science and Technology, Japan;Nara Institute of Science and Technology, Japan;Nara Institute of Science and Technology, Japan
Venue:
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Year:
2006

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Abstract

The purpose of this study is to propose Context-Aware Support Vector Machine (C-SVM) for application in a context-dependent recommendation system. It is important to consider users' contexts in information recommendation as users' preference change with context. However, currently there are few methods which take into account users' contexts (e.g. time, place, the situation and so on). Thus, we extend the functionality of a Support Vector Machines (SVM), a popular classifier method used between two classes, by adding axes of context to the feature space in order to consider the users' context. We then applied the Context-Aware SVM (C-SVM) and the Collaborative Filtering System with Context-Aware SVM (C-SVM-CF) to a recommendation system for restaurants and then examined the effectiveness of each approach.