Item Filtering System Using Associative Relation Clustering Split Method

연관관계 군집 분할 방법을 이용한 아이템 필터링 시스템

  • Published : 2007.06.28


In electronic commerce, it is important for users to recommend the proper item among large item sets with saving time and effort. Therefore, if the recommendation system can be recommended the suitable item, we will gain a good satisfaction to the user. In this paper, we proposed the associative relation clustering split method in the collaborative filtering in order to perform the accuracy and the scalability. We produce the lift between associative items using the ratings data. and then split the node group that consists of the item to improve an efficiency of the associative relation cluster. This method differs the association about the items of groups. If the association of groups is filled, the reminding items combine. To estimate the performance, the suggested method is compared with the K-means and EM in the MovieLens data set.


Collaborative Filtering;Clustering;Association Rule;Recommendation System;Electronic Commerce