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2003 IEEE/WIC International Conference on Web Intelligence (WI'03)
A Recommendation Algorithm Using Multi-Level Association Rules
Halifax, Canada
October 13-October 17
ISBN: 0-7695-1932-6
Choonho Kim, Dongguk University
Juntae Kim, Dongguk University
Recommendation systems predict user?s preference to suggest items. Collaborative filtering is the most popular method in implementing a recommendation system. The collaborative filtering method computes similarities between users based on each user?s known preference, and recommends the items preferred by similar users. Although the collaborative filtering method generally shows good performance, it suffers from two major problems - data sparseness and scalability. In this paper, we present a model-based recommendation algorithm that uses multi-level association rules to alleviate those problems. In this algorithm, we build a model for preference prediction by using association rule mining. Multi-level association rules are used to compute preferences for items. The experimental results show that applying multi-level association rules is effective, and performance of the algorithm is improved compared with the collaborative filtering method in terms of the recall and the computation time.
Citation:
Choonho Kim, Juntae Kim, "A Recommendation Algorithm Using Multi-Level Association Rules," wi, pp.524, 2003 IEEE/WIC International Conference on Web Intelligence (WI'03), 2003
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