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학술대회 Automatic Topic-based CF Recommendation Method Considering Subject Similarity
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노경주, 문경덕, 정현태
International Conference on Ubiquitous Robots and Ambient Intelligence (URAI) 2017, pp.429-432
17ZS1800, 자율성장 휴먼증강 인지컴퓨팅 기술 개발, 박전규
This paper proposes an automatic Topic-based CF Recommendation (TCFR) method for constructing a combined media with multiple unit media. To predict the preference rating score of the retrieved unit media, the proposed method uses subject similarity of the existing media combination that has been included the unit media and the target media. The subject similarity is determined by the similarity of topic-vectors that represent a set of subject topics of the media. It distinguishes a group of similar media combinations and other one using the similarity of topic vector. The method predicts the final rating score by applying weight value for the rating score that is predicted in the each group based on the typical CF (Collaborative Filtering). In the experiment of this paper, it demonstrates that the proposed context-aware recommendation method considering the subject similarity improves the performance of recommendation more than does the typical user-based CF method.
KSP 제안 키워드
Collaborative filtering(CF), Group based, Recommendation method, Topic vector, Topic-based, context-aware Recommendation, rating score, user-based CF, weight value