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An Integrated Perspective of User Evaluating Personalized Recommender Systems : Performance-Driven or User-Centric

개인화 추천시스템의 사용자 평가에 대한 통합적 접근 : 시스템 성과와 사용자 태도를 기반으로

  • 최재원 (연세대학교 정보대학원) ;
  • 이홍주 (가톨릭대학교 경영학과)
  • Received : 2012.08.16
  • Accepted : 2012.08.21
  • Published : 2012.08.31

Abstract

This study focused on user evaluation for personalized recommender systems with the integrated view of performance of the system and user attitude of recommender systems. Since users' evaluations of recommender systems can be affected by recommendation outcomes and presentation methods, both system performances based on outcomes and user attitudes formed by the presentation methods should be considered when explaining users' evaluations. However, an integrated view of system performance and user attitudes has not been applied to explain users' evaluation of recommender systems. Thus, the goal of this study is to explain users' evaluations of recommender systems under the integrated view of predictive features and explanation features at the same time. Our findings suggest that social presence, both accuracy and noveltyhave impacts onuser satisfaction for recommender systems. Especially, predictive features including accuracy and novelty affected user satisfaction. Novelty as well as accuracy is one of the significant factors for user satisfaction while recommender systems provided usual items users have experienced when systems provide serendipitous items. Likewise, explanation features with social presence and self-reference were important for user evaluation of personalized recommender systems. For explanation features, while social presence appears as one of important factors to user satisfaction of evaluating personalized recommendations, self-reference has no significant effect on user's satisfaction for recommender systems when compared to the result of social presence. Self-referencing messages did not affect user satisfaction but the levels of self-referencing are different between low and high groups in the experiment.

Keywords

Personalization;Movie Recommendations;Social Presence;Self-Reference;User Satisfaction

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