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A Local Information Passing Clustering Algorithm for Tagging Systems

  • Yu Zong
  • Guandong Xu
  • Ping Jin
  • Peter Dolog
  • Shan Jiang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6637)

Abstract

Under social tagging systems, a typical Web2.0 application, users label digital data sources by using tags which are freely chosen textual descriptions. Tags are used to index, annotate and retrieve resource as an additional metadata of resource. Poor retrieval performance remains a major problem of most social tagging systems resulting from the severe difficulty of ambiguity, redundancy and less semantic nature of tags. Clustering method is a useful tool to increase the ability of information retrieval in the aforementioned systems. In this paper, we propose a novel clustering algorithm named LIPC (Local Information Passing Clustering algorithm). The main steps of LIPC are: (1) we estimate a KNN neighbor directed graph G of tags and calculate the kernel density of each tag in its neighborhood; (2) we generate local information, local coverage and local kernel of each tag; (3) we pass the local information on G by I and O operators until they are converged and tag priory are generated; (4) we use tag priory to find out the clusters of tags. Experimental results on two real world datasets namely MedWorm and MovieLens demonstrate the efficiency and the superiority of the proposed method.

Keywords

Local Information Recommender System Kernel Density Real World Dataset Local Coverage 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Gemmell, J., Shepitsen, A., Mobasher, M., Burke, R.: Personalization in folksonomies based on tag clustering. In: Proceedings of the 6th Workshop on Intelligent Techniques for Web Personalization and Recommender Systems (July 2008)Google Scholar
  2. 2.
    Shepitsen, A., Gemmell, J., Mobasher, B., Burke, R.: Personalized recommendation in social tagging systems using hierarchical clustering. In: RecSys 2008: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 259–266. ACM, New York (2008)CrossRefGoogle Scholar
  3. 3.
    Hayes, C., Avesani, P.: Using tags and clustering to identify topic-relevant blogs. In: International Conference on Weblogs and Social Media (March 2007)Google Scholar
  4. 4.
    Chen, H., Dumais, S.: Bringing order to the web: automatically categorizing search results. In: CHI 2000: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 145–152. ACM, New York (2000)Google Scholar
  5. 5.
    van Dam, J.W., Vandic, D., Hogenboom, F., et al.: Searching and browsing tag spaces using the semantic tag clustering search framework. In: IEEE Fourth International Conference on Semantic Computing (2010)Google Scholar
  6. 6.
    Lehwark, P., Risi, S., Ultsch, A.: Visualization and Clustering of Tagged Music Data, pp. 673–680. GfKl, Berlin (2007)Google Scholar
  7. 7.
    Miao, G.X., Tatemura, J.C., Hsiung, W.P., et al.: Extracting data records from the web using tag path clustering. In: Proceedings of the 18th International Conference on World Wide Web, Spain (April 2009)Google Scholar
  8. 8.
    Giannakidou, E., Koutsonikola, V., Vakali, A., et al.: Co-clustering tags and social data sources. In: 9th International Conference on Web-age Information Managemnet, pp. 317–324 (July 2008)Google Scholar
  9. 9.
    Guan, Z., Bu, J., Mei, Q., et al.: Personalized tag recommendation using graph-based ranking on multi-type interrelated objects. In: Allan et al. [1], pp. 540–547Google Scholar
  10. 10.
    Guan, Z., Wang, C., Bu, J., et al.: Document recommendation in social tagging services. In: Rappa, M., Jones, P., Freire, J., Charkrabarti, S. (eds.) WWW, pp. 391–400. ACM, New York (2010)Google Scholar
  11. 11.
    Mika, P.: Ontologies are us: A unified model of social networks and semantics. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 522–536. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Lin, X., Guo, L., Zhao, Y.E.: Tag-based social interest discovery. In: Proceeding of the 17th International World Wide Web Conference (2008)Google Scholar
  13. 13.
    Liu, H., Lafferty, J., Wasserman, L.: Sparse nonparametric density estimation in high dimensions using the rodeo. In: 11th International Conference on Artificial Intelligence and Statistics, AISTATS (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yu Zong
    • 1
    • 2
  • Guandong Xu
    • 3
  • Ping Jin
    • 1
  • Peter Dolog
    • 3
  • Shan Jiang
    • 1
  1. 1.Department of Information and EngineeringWest Anhui UniversityLuanChina
  2. 2.Department of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefeiChina
  3. 3.Computer Science DepartmentIWIS-Intelligent Web and Information Systems, Aalborg UniversityAalborgDenmark

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