Effective User Clustering Algorithm for Collaborative Filtering System 


Vol. 8,  No. 2, pp. 144-154, Apr.  2001
10.3745/KIPSTB.2001.8.2.144


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  Abstract

Grouping user into clusters based on the web documents they have retrieved allows accurate recommendations of new web documents. A variety of algorithms have previously been designed with shortcomings of slow speed or low accuracy. For the purpose of complementing these shortcomings in this paper, we propose CUG algorithm, which is effective user clustering algorithm for collaborative filtering system. CUG algorithm uses Apriori algorithm, Naive Bayes algorithm in order to make clusters for users. Apriori algorithm constructs association word knowledge base, Naive Bayes algorithm adds weight to the association word knowledge base and categorizes web documents retrieved by user into classes. CUG algorithm groups users into clusters based on these web documents. CUG algorithm designed through these methods can be used to improve the efficiency of information retrieval by prefetching documents for the users and storing them in a document database in the system. For the purpose of evaluating performance for CUG algorithm designed in this paper, we compare our algorithm with methods of K-means clustering and Gibbs sampling.

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[IEEE Style]

S. J. Ko, K. W. Rim, J. H. Lee, "Effective User Clustering Algorithm for Collaborative Filtering System," The KIPS Transactions:PartB , vol. 8, no. 2, pp. 144-154, 2001. DOI: 10.3745/KIPSTB.2001.8.2.144.

[ACM Style]

Su Jeong Ko, Kee Wook Rim, and Jung Hyun Lee. 2001. Effective User Clustering Algorithm for Collaborative Filtering System. The KIPS Transactions:PartB , 8, 2, (2001), 144-154. DOI: 10.3745/KIPSTB.2001.8.2.144.

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