Content-Based Image Filtering for Recommendation
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.
KeywordsRecommendation System Color Histogram Collaborative Filter Mean Absolute Error Bayesian Classifier
Unable to display preview. Download preview PDF.
- 1.Balabanovic, M., Shoham, Y.: Fab: Content-based, Collaborative Recommendation. Communication of the Association of Computing Machinery 40(3), 66–72 (1997)Google Scholar
- 3.Jung, K.Y., Lee, J.H.: User Preference Mining through Hybrid Collaborative Filtering and Content-based Filtering in Recommendation System. IEICE Transaction on Information and Systems E87-D(12), 2781–2790 (2004)Google Scholar
- 4.Jung, K.Y., Park, D.H., Lee, J.H.: Personalized Movie Recommender System through Hybrid 2-Way Filtering with Extracted Information. In: Proc. of the International Conference on Flexible Query Answering Systems, pp. 473–486 (2004)Google Scholar
- 5.Ko, S.J., Lee, J.H.: User Preference Mining through Collaborative Filtering and Content Based Filtering in Recommender System. In: Proc. of the International Conference on E-Commerce and Web Technologies, pp. 244–253 (2002)Google Scholar
- 6.Kohrs, A., Merialdo, B.: Improving Collaborative Filtering with Multimedia Indexing Techniques to Create User-Adapting Web Sites. In: Proc. of the ACM International Conference on Multimedia, pp. 27–36 (1999)Google Scholar
- 7.Lee, W.S.: Collaborative Learning for Recommender Systems. In: Proc. of the 18th International Conference on Machine Learning, pp. 314–321 (2001)Google Scholar
- 8.McJones, P.: EachMovie Dataset: http://www.research.digital.com/SRC/eachmovie/
- 9.Melville, P., Mooney, R.J., Nagarajan, R.: Content-Boosted Collaborative Filtering for Improved Recommendations. In: Proc. of the National Conference on Artificial Intelligence, pp. 187–192 (2002)Google Scholar
- 11.Pazzani, M.J.: A Framework for Collaborative, Content-based and Demographic Filtering. Artificial Intelligence Review 408, 5–6 (1999)Google Scholar
- 12.Wang, J., de Vries, A.P., Reinders, M.J.T.: A User-Item Relevance Model for Log-based Collaborative Filtering. In: Proc. of the European Conference on Information Retrieval, pp. 37–48 (2006)Google Scholar