A Collaborative Filtering Recommendation Algorithm Based on User Clustering in E-Commerce Personalized Systems


Article Preview

Electronic commerce recommender systems are becoming increasingly popular with the evolution of the Internet, and collaborative filtering is the most successful technology for building recommendation systems. Unfortunately, the efficiency of this method declines linearly with the number of users and items. So, as the magnitudes of users and items grow rapidly, the result in the difficulty of the speed bottleneck of collaborative filtering systems. In order to raise service efficiency of the personalized systems, a collaborative filtering recommendation method based on clustering of users is presented. Users are clustered based on users ratings on items, then the nearest neighbors of target user can be found in the user clusters most similar to the target user. Based on the algorithm, the collaborative filtering algorithm should be divided into two stages, and it separates the procedure of recommendation into offline and online phases. In the offline phase, the basic users are clustered into centers; while in the online phase, the nearest neighbors of an active user are found according to the basic users’ cluster centers, and the recommendation to the active user is produced.



Edited by:

Yanwen Wu




G. H. Cheng, "A Collaborative Filtering Recommendation Algorithm Based on User Clustering in E-Commerce Personalized Systems", Advanced Materials Research, Vol. 267, pp. 789-793, 2011

Online since:

June 2011





[1] Songjie Gong, An Efficient Collaborative Recommendation Algorithm Based on Item Clustering, Lecture notes in electrical engineering, Volume 72, pp: 381-387.

DOI: https://doi.org/10.1007/978-3-642-14350-2_48

[2] Xue, G., Lin, C., & Yang, Q., et al. Scalable collaborative filtering using cluster-based smoothing. In Proceedings of the ACM SIGIR Conference 2005 p.114–121.

[3] Songjie Gong, Employing User Attribute and Item Attribute to Enhance the Collaborative Filtering Recommendation, Journal of Software, Volume 4, Number 8, October 2009, pp: 883-890.

DOI: https://doi.org/10.4304/jsw.4.8.883-890

[4] K Honda, N Sugiura, H Ichihashi, S Araki. Collaborative Filtering Using Principal Component Analysis and Fuzzy Clustering, Lecture Notes in Computer Science, (2001).

DOI: https://doi.org/10.1007/3-540-45490-x_50

[5] B. Sarwar, G. Karypis, J. Konstan and J. Riedl, Recommender systems for large-scale e-commerce: Scalableneighborhood formation using clustering, Proceedings of the Fifth International Conference on Computer andInformation Technology, (2002).

[6] Songjie Gong, A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering, Journal of Software, Volume 5, Number 7, July 2010, pp: 745-752.

DOI: https://doi.org/10.4304/jsw.5.7.745-752

[7] Songjie Gong, Personalized Recommendation System Based on Association Rules Mining and Collaborative Filtering, Applied Mechanics and Materials, Volume 39, pp: 540-544.

DOI: https://doi.org/10.4028/www.scientific.net/amm.39.540

[8] Songjie Gong, An Enhanced Similarity Measure Used in Personalized Recommendation Algorithms, Advanced Materials Research, Volume 159, pp: 671-675.

DOI: https://doi.org/10.4028/www.scientific.net/amr.159.671

[9] L. H. Ungar and D. P. Foster. A Formal Statistical Approach to Collaborative Filtering. Proceedings of Conference on Automated Leading and Discovery (CONALD), (1998).

[10] M. O. Conner and J. Herlocker. Clustering Items for Collaborative Filtering. In Proceedings of the ACM SIGIR Workshop on Recommender Systems, Berkeley, CA, August (1999).

[11] A. Kohrs and B. Merialdo. Clustering for Collaborative Filtering Applications. In Proceedings of CIMCA'99. IOS Press, (1999).

[12] Lee, WS. Online clustering for collaborative filtering. School of Computing Technical Report TRA8/00. (2000).

[13] S.H.S. Chee,J Han,K. Wang.Rectree: An efficient collaborative filtering method. Lecture Notes in Computer Science, 2114, (2001).

[14] D. Bridge and J. Kelleher, Experiments in sparsity reduction: Using clustering in collaborative recommenders, in Procs. of the Thirteenth Irish Conference on Artificial Intelligence and Cognitive Science, p.144–149. Springer, (2002).

DOI: https://doi.org/10.1007/3-540-45750-x_18

[15] J. Kelleher and D. Bridge. Rectree centroid: An accurate, scalable collaborative recommender. In Procs. of the Fourteenth Irish Conference on Artificial Intelligence and Cognitive Science, pages 89–94, (2003).

[16] George, T., & Merugu, S. A scalable collaborative filtering framework based on co-clustering. In Proceedings of the IEEE ICDM Conference. (2005).

DOI: https://doi.org/10.1109/icdm.2005.14