Clustering Users According to Common Interest Based on User Search Behavior


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The paper presents a novel method to cluster users who share the common interest and discover their common interest domain by mining different users’ search behaviors in the user session, mainly the consecutive search behavior and the click sequence considering the click order and the syntactic similarity. The community is generated and this information will be used in the recommendation system in the future. Also the method is ‘content-ignorant’ to avoid the storage and manipulation of a large amount of data when clustering the web pages by content. The experiment proved it an available and effective way.



Advanced Materials Research (Volumes 143-144)

Edited by:

H. Wang, B.J. Zhang, X.Z. Liu, D.Z. Luo, S.B. Zhong




P. Y. Zhang et al., "Clustering Users According to Common Interest Based on User Search Behavior", Advanced Materials Research, Vols. 143-144, pp. 851-855, 2011

Online since:

October 2010




[1] Beitzel, S. M., Jensen, E. C.: Automatic classification of Web queries using very large unlabeled query logs , ACM Transactions on Information Systems, (2007).


[2] W. Bruce Croft, Donald metzler, Search Engines Information Retrieval in Practice, China Machine Press, Beijing, (2009).

[3] A Joshi, K Joshi: On accessing the log files , In ACM SIGMOD Workshop, (2000).

[4] T. Lau, E. Horvitz, Patterns of search: analyzing and modeling web query refinement , In: UM '99: Proceedings of the Seventh International Conference on User Modeling, 1999, pp.119-128.


[5] D. Beeferman, A. Berger: Agglomerative clustering of a search engine query log , in: KDD'00: Proceedings of the Sixth ACMSIGKDD International Conference on Knowledge Discovery and Data Mining, 2000, pp.407-416.


[6] G. Demange: Sharing information in Web communities , Games and Economic Behavior 68 (2010) 580-601.


[7] M. Bertier et al.: Toward personalized query expansion , European Conference on Computer Systems, Proceedings of the Second ACM EuroSys Workshop on Social Network Systems (2009) 7-12.


[8] Xiaoming Li, Hongfei Yan, Jimin Wang, Search Engine: Principle, Technology and System. , Science press, Beijing, (2005).

[9] Z. Zhang, O. Nasraoui: Mining search engine query logs for social filtering-based query , Applied Soft Computing 8 (2008) 1326-1334.


[10] Y. Zhang et al.: Time series analysis of a Web search engine transaction log , Information Processing and Management 45 (2009) 230-245.


[11] Baeza, R., & Ribeiro, B., Modern information retrieval. China Machine Press, Beijing, (1999).