Abstract
The ever-increasing popularity of the Internet has led to an explosive growth of the sheer volume of information. Recommender system is one of the possible solutions to the information overload problem. Traditional item-based collaborative filtering algorithms can provide quick and accurate recommendations by building a model offline. However, they may not be able to provide truly personalized information. For providing efficient and effective recommendations while maintaining a certain degree of personalization, in this paper, we propose a hybrid model-based recommender system which first partitions the user set based on user ratings and then performs item-based collaborative algorithms on the partitions to compute a list of recommendations. We have applied our system to the well known movielens dataset. Three measures (precision, recall and F1-measure) are used to evaluate the performance of the system. The experimental results show that our system is better than traditional collaborative recommender systems.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Jamshid, B.: Browsing through public access catalogs. Information Technology & Libraries 11(3), Library and Information Technology Association, 220–228 (1992)
Wen, J.-R., Nie, J.-Y., Zhang, H.-J.: Clustering user queries of a search engine. In: Proceedings of Conference on World Wide Web, pp. 162–168. ACM Press, New York (2001)
Billsus, D., Pazzani, M.J.: Learning collaborative information filters. In: Proceedings of ICML, pp. 46–53. Morgan Kaufmann Publishers Inc., San Francisco (1998)
Ungar, L.H., Foster, D.P.: Clustering methods for collaborative filtering. In: Proceedings of Workshop on Recommendation Systems, pp. 59–62. AAAI Press, Menlo Park (1998)
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.T.: Analysis of recommendation algorithms for e-commerce. In: Proceedings of ACM EC, pp. 158–167. ACM Press, New York (2000)
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.T.: Item-based collaborative filtering recommendation algorithms. In: Proceedings International WWW Conference, pp. 285–295. ACM Press, New York (2001)
Deshpande, M., Karypis, G.: Item-based top-N recommendation algorithms. ACM Transactions on Information Systems 22(1), 143–177 (2004)
Miyahara, K., Pazzani, M.: Collaborative Filtering with the Simple Bayesian Classifier. In: Mizoguchi, R., Slaney, J.K. (eds.) PRICAI 2000. LNCS, vol. 1886, pp. 679–689. Springer, Heidelberg (2000)
O’Connor, M., Herlocker, J.: Clustering items for collaborative filtering. In: Proceedings of Workshop on Recommendation Systems. AAAI Press, Menlo Park (1999)
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.T.: Application of Dimensionality Reduction in Recommender System – A Case Study. In: ACM Web Mining for E-Commerce Workshop. ACM Press, New York (2000)
Agrawal, R., Imielinski, T., Swami, A.: Mining Associations between Sets of Items in Large Databases. In: Proceedings of the ACM-SIGMOD International Conference on Management of Data, pp. 207–216. ACM Press, New York (1993)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th VLDB, pp. 487–499. Morgan Kaufmann Publishers Inc., San Francisco (1994)
Han, E.-H., Karypis, G., Kumar, V., Mobasher, B.: Clustering based on association rule hypergraphs. In: Proceedings of SIGMOD Workshop on Rresearch Issues in Data Mining and Knowledge Discovery, pp. 9–13. Springer, Heidelberg (1997)
Karypis, G., Kumar, V.: Multilevel hypergrph partitioning: Application in VLSI domain. IEEE Transaction on VLSI Systems 7, 69–79 (1999)
Aguzzoli, S., Avesani, P., Gerla, B.: A logical framework for fuzzy collaborative filtering. In: Proceedings of The 10th IEEE International Conference on Fuzzy Systems, pp. 1043–1046. IEEE Computer Society, Los Alamitos (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hwang, CS., Tsai, PJ. (2005). A Collaborative Recommender System Based on User Association Clusters. In: Ngu, A.H.H., Kitsuregawa, M., Neuhold, E.J., Chung, JY., Sheng, Q.Z. (eds) Web Information Systems Engineering – WISE 2005. WISE 2005. Lecture Notes in Computer Science, vol 3806. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11581062_36
Download citation
DOI: https://doi.org/10.1007/11581062_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30017-5
Online ISBN: 978-3-540-32286-3
eBook Packages: Computer ScienceComputer Science (R0)