New approaches to mood-based hybrid collaborative filtering

Authors:
Licai Wang;Xiangwu Meng;Yujie Zhang;Yancui Shi
Affiliations:
Beijing University of Posts and Telecommunications, Beijing, China;Beijing University of Posts and Telecommunications, Beijing, China;Beijing University of Posts and Telecommunications, Beijing, China;Beijing University of Posts and Telecommunications, Beijing, China
Venue:
Proceedings of the Workshop on Context-Aware Movie Recommendation
Year:
2010

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Abstract

Recently, mood has proved to be an important contextual feature in context-aware recommender systems (CARS) by some studies. In this paper we propose two new approaches to mood-based hybrid collaborative filtering (CF) in order to further improve the performance accuracy and user satisfaction by utilizing emotional context in CARS. We first describe the traditional user-based CF as the baseline approach, and then propose a new mood-based user-based CF which detects user preferences to each emotion. On this basis, we propose two hybrid CF approaches using multiple-step nearest neighbors search and predicted ratings fusion strategies respectively. We perform experimental comparisons of the above approaches on the Moviepilot dataset released for the Challenge on Context-Aware Movie Recommendation (CAMRa2010). The results suggest that both hybrid approaches provide improvements in performance.