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Volume 28 Issue 5
September  2013
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Wen Qu, Kai-Song Song, Yi-Fei Zhang, Shi Feng, Da-Ling Wang, Ge Yu. A Novel Approach Based on Multi-View Content Analysis and SemiSupervised Enrichment for Movie Recommendation[J]. Journal of Computer Science and Technology, 2013, 28(5): 776-787. DOI: 10.1007/s11390-013-1376-7
Citation: Wen Qu, Kai-Song Song, Yi-Fei Zhang, Shi Feng, Da-Ling Wang, Ge Yu. A Novel Approach Based on Multi-View Content Analysis and SemiSupervised Enrichment for Movie Recommendation[J]. Journal of Computer Science and Technology, 2013, 28(5): 776-787. DOI: 10.1007/s11390-013-1376-7
shu

A Novel Approach Based on Multi-View Content Analysis and SemiSupervised Enrichment for Movie Recommendation

  • School of Information Science and Engineering, Northeastern University, Shenyang 110819, China

Funds: Supported by the National Basic Research 973 Program of China under Grant No. 2011CB302200-G, the Key Program of National Natural Science Foundation of China under Grant No. 61033007, the National Natural Science Foundation of China under Grant Nos. 61100026, 60973019, and the Fundamental Research Funds for the Central Universities of China under Grant Nos. N110604003, N100704001, N100304004, N120404007.
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  • Received Date: May 04, 2013
  • Revised Date: August 13, 2013
  • Published Date: September 04, 2013
    Abstract
  • Although many existing movie recommender systems have investigated recommendation based on information such as clicks and tags, much less efforts have been made to explore the multimedia content of movies, which has potential information for the elicitation of the user's visual and musical preferences. In this paper, we explore the content from three media types (image, text, audio) and propose a novel multi-view semi-supervised movie recommendation method, which represents each media type as a view space for movies. The three views of movies are integrated to predict the rating values under the multi-view framework. Furthermore, our method considers the casual users who rate limited movies. The algorithm enriches the user profile with a semi-supervised way when there are only few rating histories. Experiments indicate that the multimedia content analysis reveals the user's profile in a more comprehensive way. Different media types can be a complement to each other for movie recommendation. And the experimental results validate that our semi-supervised method can effectively enrich the user profile for recommendation with limited rating history.
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