Content-Based image filtering for recommendation

Authors:
Kyung-Yong Jung
Affiliations:
School of Computer Information Engineering, Sangji University, Korea
Venue:
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Year:
2006

Citing 9
Cited 1

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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.