Accurate Estimation of Personalized Video Preference Using Multiple Users' Viewing Behavior

DOI Web Site 被引用文献2件 参考文献33件 オープンアクセス
  • OGAWA Takahiro
    Graduate School of Information Science and Technology, Hokkaido University
  • ITO Yoshiki
    Graduate School of Information Science and Technology, Hokkaido University
  • HASEYAMA Miki
    Graduate School of Information Science and Technology, Hokkaido University

書誌事項

公開日
2018
資源種別
journal article
DOI
  • 10.1587/transinf.2017edp7178
公開者
一般社団法人 電子情報通信学会

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説明

<p>A method for accurate estimation of personalized video preference using multiple users' viewing behavior is presented in this paper. The proposed method uses three kinds of features: a video, user's viewing behavior and evaluation scores for the video given by a target user. First, the proposed method applies Supervised Multiview Spectral Embedding (SMSE) to obtain lower-dimensional video features suitable for the following correlation analysis. Next, supervised Multi-View Canonical Correlation Analysis (sMVCCA) is applied to integrate the three kinds of features. Then we can get optimal projections to obtain new visual features, “canonical video features” reflecting the target user's individual preference for a video based on sMVCCA. Furthermore, in our method, we use not only the target user's viewing behavior but also other users' viewing behavior for obtaining the optimal canonical video features of the target user. This unique approach is the biggest contribution of this paper. Finally, by integrating these canonical video features, Support Vector Ordinal Regression with Implicit Constraints (SVORIM) is trained in our method. Consequently, the target user's preference for a video can be estimated by using the trained SVORIM. Experimental results show the effectiveness of our method.</p>

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