International Journal of Education and Management Engineering(IJEME)

ISSN: 2305-3623 (Print), ISSN: 2305-8463 (Online)

Published By: MECS Press

IJEME Vol.2, No.6, Jun. 2012

Tag Recommendation Based on Collaborative Filtering and Text Similarity

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Chuanbao Wang,Fang Yuan,Ying Yun

Index Terms

Component;Tagging system;Tag;Tag recommended;Webpage


In current social tagging system, users can freely add tags for the uploaded resources, which caused a problem that many tags could not describe the resource properly and even have some spelling errors. This problem may bring unnecessary troubles for other users who want to search this kind of resource. In this paper, a tag recommendation system based on collaborative filtering and text similarity is presented to solve the problem mentioned above. This system can automatically recommend some relevant tags for the new uploaded resources and thus the users can freely select tags from the system. Experimental results show that the recommended tags can effectively represent the contents of the webpages marked. Compared with the existing tag recommended methods, this method not only improves the accuracy of tags recommended, but also facilitates the webpage sharing and retrieval.

Cite This Paper

Chuanbao Wang,Fang Yuan,Ying Yun,"Tag Recommendation Based on Collaborative Filtering and Text Similarity", IJEME, vol.2, no.6, pp.7-14, 2012.


[1]Golder, S., and Huberman, B., “The structure of collaborative tagging systems,” Journal of Information Science, vol. 32, pp. 198-208, 2006.

[2]Xu, Z., “Towards the semantic web: Collaborative tag suggestions,” In Proceedings of the Collaborative Web Tagging Workshop, pp. 22-26, 2006.

[3]Sigma On Kee Lee, and Andy Hon Wai Chun, “A web 2.0 tag recommendation algorithm using hybrid ANN semantic structures,” International Journal of Computers, vol. 1, pp. 49-58, 2007.

[4]Yu-Ta Lu, Shoou-I Yu, and Tsung-Chieh Chang, “A Content-based Method to Enhance Tag Recommendation,” In Proceedings of IJCAI'09, pp. 2064-2069, 2009.

[5]Porter MF., “An algorithm for suffix stripping,” Program, vol. 14, pp. 130-137, 1980.

[6]Breiman L, Friedman J, and Olshen R, “Classification and regression tress,” Monterey: Wadsworth International Group, 1984.

[7]G. Salton, A. Wong and C. S. Yang, “A vector space model for automatic indexing,” Communications of the ACM, vol. 18 pp. 613-620, 1975.

[8]Jiawei Han, and Micheline Kamber, “Data Mining Concepts and Techniques,” China: Machine Press, 2008.


[10]S Guha, R Rastogi, and K Shim, “An Efficient Clustering Algorithm for Large Databases,” In Proceedings of the ACM SIGMOD international conference on management of data, pp. 73-84, 1998.