Performance of Collaborative Filtering Agent System using Clustering for Better Recommendations 


Vol. 7,  No. 5, pp. 1599-1608, May  2000
10.3745/KIPSTE.2000.7.5.1599


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  Abstract

Automated collaborative filtering is on the verge of becoming a popular technique to reduce overloaded information as well as to solve the problems that content-based information filtering systems cannot handle. In this paper, we describe three different algorithms that perform collaborative filtering: GroupLens that is the traditional technique; Best N, the modified one; and an algorithm that uses clustering. Based on the experimental results using real data, the algorithm using clustering is compared with the existing representative collaborative filtering agent algorithms such as GroupLens and Best N. The experimental results indicate that the algorithm using clustering is similar to Best N and better than GroupLens for prediction accuracy. The results also demonstrate that the algorithm using clustering produces the best performance according to the standard deviation of error rate. This means that the algorithm using clustering gives the most stable and the best uniform recommendation. In addition, the algorithm using clustering reduces the time for recommendation.

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[IEEE Style]

B. Y. Hwang, "Performance of Collaborative Filtering Agent System using Clustering for Better Recommendations," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 7, no. 5, pp. 1599-1608, 2000. DOI: 10.3745/KIPSTE.2000.7.5.1599.

[ACM Style]

Byung Yeon Hwang. 2000. Performance of Collaborative Filtering Agent System using Clustering for Better Recommendations. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 7, 5, (2000), 1599-1608. DOI: 10.3745/KIPSTE.2000.7.5.1599.

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