@inproceedings{3d85092aac3c4e798cb0a7c7f280afe7,
title = "A recommendation algorithm using multi-level association rules",
abstract = "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. We present a model-based recommendation algorithm that uses multilevel association rules to alleviate those problems. In this algorithm, we build a model for preference prediction by using association rule mining. Multilevel association rules are used to compute preferences for items. The experimental results show that applying multilevel 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.",
keywords = "Association rules, Bayesian methods, Collaboration, Data mining, Filtering algorithms, Information analysis, Performance analysis, Predictive models, Scalability, Web pages",
author = "Choonho Kim and Juntae Kim",
note = "Publisher Copyright: {\textcopyright} 2003 IEEE.; IEEE/WIC International Conference on Web Intelligence, WI 2003 ; Conference date: 13-10-2003 Through 17-10-2003",
year = "2003",
doi = "10.1109/WI.2003.1241257",
language = "English",
series = "Proceedings - IEEE/WIC International Conference on Web Intelligence, WI 2003",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "524--527",
editor = "Jiming Liu and Nick Cercone and Matthias Klusch and Chunnian Liu and Ning Zhong",
booktitle = "Proceedings - IEEE/WIC International Conference on Web Intelligence, WI 2003",
address = "United States",
}