Categorization for Grouping Associative Items Mining in Item-Based Collaborative Filtering

Recommendation systems have been investigated and implemented in many aspects. Particularly, in case of collaborative filtering system, more important issue is how to manipulate the personalized recommendation results for better user understandability and satisfaction. Collaborative filtering system predicts items of interest for users based on predictive relationship discovered between the item and others. In this paper, the categorization for grouping associative items mining, for the purpose of improving accuracy and performance in the item-based collaborative filtering, is proposed. It is possible that, if the associative item is required to be simultaneously regrouped in all other groups in which they occur, the proposed method regrouped the associative items into the relevant group. In addition, the proposed method can result in improved predictive performance under the sparse data and cold-start circumstance that starts from small items in the collaborative filtering. And this method can increase the prediction accuracy and the scalability because of removing the noise generated by ratings on items of dissimilar content or interest. The approach is empirically evaluated for comparison with k-means, average link, and robust, using the MovieLens dataset. This method was found to significantly outperform the previous method.