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Content-Based Image Filtering for Recommendation

  • Kyung-Yong Jung
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4203)

Abstract

Content-based filtering can reflect content information, and provide recommendations by comparing various feature based information regarding an item. However, this method suffers from the shortcomings of superficial content analysis, the special recommendation trend, and varying accuracy of predictions, which relies on the learning method. In order to resolve these problems, this paper presents content-based image filtering, seamlessly combining content-based filtering and image-based filtering for recommendation. Filtering techniques are combined in a weighted mix, in order to achieve excellent results. In order to evaluate the performance of the proposed method, this study uses the EachMovie dataset, and is compared with the performance of previous recommendation studies. The results have demonstrated that the proposed method significantly outperforms previous methods.

Keywords

Recommendation System Color Histogram Collaborative Filter Mean Absolute Error Bayesian Classifier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kyung-Yong Jung
    • 1
  1. 1.School of Computer Information EngineeringSangji UniversityKorea

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