On Kernel Information Propagation for Tag Clustering in Social Annotation Systems

  • Guandong Xu
  • Yu Zong
  • Rong Pan
  • Peter Dolog
  • Ping Jin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6882)


In social annotation systems, users label digital resources by using tags which are freely chosen textual descriptors. Tags are used to index, annotate and retrieve resource as an additional metadata of resource. Poor retrieval performance remains a major challenge of most social annotation systems resulting from the severe problems of ambiguity, redundancy and less semantic nature of tags. Clustering method is a useful approach to handle these problems in the social annotation systems. In this paper, we propose a novel clustering algorithm named kernel information propagation for tag clustering. This approach makes use of the kernel density estimation of the KNN neighbor directed graph as a start to reveal the prestige rank of tags in tagging data. The random walk with restart algorithm is then employed to determine the center points of tag clusters. The main strength of the proposed approach is the capability of partitioning tags from the perspective of tag prestige rank rather than the intuitive similarity calculation itself. Experimental studies on three real world datasets demonstrate the effectiveness and superiority of the proposed method.


Recommender System Kernel Density Real World Dataset Depth First Search Random Walk With Restart 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chen, H., Dumais, S.: Bringing order to the web: Automatically categorizing search results. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 145–152. ACM, New York (2000)CrossRefGoogle Scholar
  2. 2.
    van Dam, J., Vandic, D., Hogenboom, F., Frasincar, F.: Searching and browsing tag spaces using the semantic tag clustering search framework. In: IEEE Fourth International Conference on Semantic Computing (ICSC), pp. 436–439. IEEE, Los Alamitos (2010)Google Scholar
  3. 3.
    Durao, F., Dolog, P.: Extending a hybrid tag-based recommender system with personalization. In: SAC 2010: Proceedings of the 2010 ACM Symposium on Applied Computing, pp. 1723–1727. ACM, New York (2010)Google Scholar
  4. 4.
    Guan, Z., Bu, J., Mei, Q., Chen, C., Wang, C.: Personalized tag recommendation using graph-based ranking on multi-type interrelated objects. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 540–547. ACM, New York (2009)Google Scholar
  5. 5.
    Guan, Z., Wang, C., Bu, J., Chen, C., Yang, K., Cai, D., He, X.: Document recommendation in social tagging services. In: Proceedings of the 19th International Conference on World Wide Web, pp. 391–400. ACM, New York (2010)Google Scholar
  6. 6.
    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
  7. 7.
    Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., Stumme, G.: Tag recommendations in folksonomies. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 506–514. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    Lehwark, P., Risi, S., Ultsch, A.: Visualization and clustering of tagged music data. Data Analysis, Machine Learning and Applications, 673–680 (2008)Google Scholar
  9. 9.
    Liu, H., Lafferty, J., Wasserman, L.: Sparse nonparametric density estimation in high dimensions using the rodeo. In: Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, San Juan, Puerto Rico (2007)Google Scholar
  10. 10.
    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
  11. 11.
    Noll, M.G., Meinel, C.: Web search personalization via social bookmarking and tagging. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 367–380. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Shepitsen, A., Gemmell, J., Mobasher, B., Burke, R.: Personalized recommendation in social tagging systems using hierarchical clustering. In: Proceedings of the 2008 ACM Conference on Recommender systems, pp. 259–266. ACM, New York (2008)CrossRefGoogle Scholar
  13. 13.
    Sun, J., Qu, H., Chakrabarti, D., Faloutsos, C.: Neighborhood formation and anomaly detection in bipartite graphs. In: ICDM, pp. 418–425 (2005)Google Scholar
  14. 14.
    Tso-Sutter, K.H.L., Marinho, L.B., Schmidt-Thieme, L.: Tag-aware recommender systems by fusion of collaborative filtering algorithms. In: SAC 2008: Proceedings of the 2008 ACM Symposium on Applied Computing. pp. 1995–1999. ACM, New York (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Guandong Xu
    • 1
    • 3
  • Yu Zong
    • 2
    • 4
  • Rong Pan
    • 3
  • Peter Dolog
    • 3
  • Ping Jin
    • 2
  1. 1.School of Engineering & ScienceVictoria UniversityAustralia
  2. 2.Department of Information and EngineeringWest Anhui UniversityLiuanChina
  3. 3.Department of Computer ScienceAalborg UniversityDenmark
  4. 4.Department of Computer Science and TechnologyUniversity of Science and Technology of ChinaChina

Personalised recommendations