Automatic topic-based CF recommendation method considering subject similarity
Kyoung Ju Noh, Kyung-Duk Moon, Hyuntae Jeong · 2017
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.