Automatic Classification for Grouping Designs in Fashion Design Recommendation Agent System

Jung, Kyomin

doi:10.1007/11892960_38

Cited by 2 publications

(1 citation statement)

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“…In this case, before the associative item goes to another group, the similarity coefficient is calculated between the item, and the others in the current group, by the dice coefficient. If the similarity coefficient between the item and the others in current group is less than the similarity_threshold, item's group j (i) is changeless, otherwise the item goes to the next group [10]. …”

Section: Categorization For Grouping Associative Items Miningmentioning

confidence: 99%

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

Chung

Lee

Kim

2011

2011 International Conference on Information Science and Applications

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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.

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Section: Categorization For Grouping Associative Items Miningmentioning

confidence: 99%