Utilizing Non-redundant Association Rules from Multi-level Datasets
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Towards Privacy Compliant and Anytime Recommender Systems
EC-Web 2009 Proceedings of the 10th International Conference on E-Commerce and Web Technologies
A case study in a recommender system based on purchase data
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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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 forpreference 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.