Using WordNet-Based Neighborhood for Improving Social Tag Recommendation

  • Ya-Tao Zhu
  • Sheng-Hua Liu
  • Xue-Qi Cheng
  • Yue Liu
  • Yuan-Zhuo Wang
  • Jin-Gang Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7390)


Recent years have seen social tag recommendation growing into a popular service for users to organize and share digital content on social webpages. Among of knowledge discovery techniques that are applied in social tag recommendation systems, the collaborative filtering based ones are achieving widespread success. The similarity measurement is critical to determine the appropriateness of the results recommendation in the collaborative-filtering schema. In this paper, a nugget is introduced as an atomic conceptual entity generating from WordNet, to measure the similarity of web content and recommend tags. With the nuggets, we can use the WordNet-based neighbors, rather than the literal ones for collaborative filtering, which considers the common sense that the expression varies for a specific concept. The experiments conducted on the dataset from, have shown that our approach is effective and consistently achieves better precision and recall than both baselines.


Social Tag Recommendation Collaborative Filtering WordNet 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Golder, S.A., Huberman, B.A.: Usage patterns of collaborative tagging systems. J. Inf. Sci. 32(2), 198–208 (2006)CrossRefGoogle Scholar
  2. 2.
    Quintarelli, E.: Folksonomies: power to the people. ISKO Italy-UniMIB Meeting (2005)Google Scholar
  3. 3.
    Bao, S., Xue, G., Wu, X., Yu, Y., Fei, B., Su, Z.: Optimizing web search using social annotations. In: WWW 2007, pp. 501–510 (2007)Google Scholar
  4. 4.
    Zhou, D., Bian, J., Zheng, S., Zha, H., Giles, C.L.: Exploring social annotations for information retrieval. In: WWW 2008, pp. 715–724 (2008)Google Scholar
  5. 5.
    Xu, S., Bao, S., Fei, B., Su, Z., Yu, Y.: Exploring folksonomy for personalized search. In: Proc. of International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 155–162 (2008)Google Scholar
  6. 6.
    Conde, J.M., Vallet, D., Castells, P.: Inferring user intent in web search by exploiting social annotations. In: Proc. of International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 827–828 (2010)Google Scholar
  7. 7.
    Vallet, D., Cantador, I., Jose, J.M.: Personalizing Web Search with Folksonomy-Based User and Document Profiles. In: Gurrin, C., He, Y., Kazai, G., Kruschwitz, U., Little, S., Roelleke, T., Rüger, S., van Rijsbergen, K. (eds.) ECIR 2010. LNCS, vol. 5993, pp. 420–431. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Guo, J., Cheng, X., Xu, G., Shen, H.: A structured approach to query recommendation with social annotation data. In: Proc. of the ACM Conference on Information and Knowledge Management, pp. 619–628 (2010)Google Scholar
  9. 9.
    Heymann, P., Ramage, D., Garcia-Molina, H.: Social tag prediction. In: SIGIR 2008: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 531–538 (2008)Google Scholar
  10. 10.
    Lu, Y.-T., Yu, S.-I., Chang, T.-C., Hsu, J.Y.: A content-based method to enhance tag recommendation. In: Proc. of IJCAI 2009, pp. 2064–2069 (2009)Google Scholar
  11. 11.
    Marlow, C., Naaman, M., Boyd, D., Davis, M.: Position paper, tagging, taxonomy, flickr, article, toread. In: Collaborative Web Tagging Workshop at WWW 2006, Edinburgh, Scotland (2006)Google Scholar
  12. 12.
    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
  13. 13.
    Wu, X., Zhang, L., Yu, Y.: Exploring social annotations for the semantic web. In: WWW 2006, pp. 417–426 (2006)Google Scholar
  14. 14.
    Goldberg, D., Nichols, D., Oki, B., Terry, D.: Using collaborative filtering to weave an information tapestry. Communications of the ACM (1992)Google Scholar
  15. 15.
    Resnick, P., Varian, H.R.: Recommender Systems. Communications of the ACM 40(3), 56–58 (1997)CrossRefGoogle Scholar
  16. 16.
    Nakamoto, R., Nakajima, S., Miyazaki, J., Uemura, S.: Tag-based contextual collaborative filtering. IAENG International Journal of Computer Science 34(2), 214–219 (2007)Google Scholar
  17. 17.
    Fellbaum, C.: WordNet: An Electronic Lexical Database. MIT Press (1998)Google Scholar
  18. 18.
    Banerjee, S., Pedersen, T.: An adapted Lesk algorithm for word sense disambiguation using WordNet. In: Computational Linguistics and Intelligent Text Processing, pp. 117–171 (2002)Google Scholar
  19. 19.
    Locoro, A.: Tagging Ontologies with Fuzzy WordNet Domains. In: Petrosino, A. (ed.) WILF 2011. LNCS, vol. 6857, pp. 107–114. Springer, Heidelberg (2011)Google Scholar
  20. 20.
    Dang, H., Lin, J., Kelly, D.: Overview of the TREC 2006 question answering track. In: Proc. of TREC 2006 (2006)Google Scholar
  21. 21.
    Song, Y., Zhuang, Z., Li, H., Zhao, Q., Li, J., Lee, W.-C., Giles, C.L.: Real-time automatic tag recommendation. In: SIGIR 2008, pp. 515–522 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ya-Tao Zhu
    • 1
    • 2
    • 3
  • Sheng-Hua Liu
    • 2
  • Xue-Qi Cheng
    • 2
  • Yue Liu
    • 2
  • Yuan-Zhuo Wang
    • 2
  • Jin-Gang Liu
    • 1
  1. 1.Capital Normal UniversityBeijingChina
  2. 2.Institute of Computing TechnologyChinese Academy of SciencesChina
  3. 3.College of Information Science & TechnologyAgricultural University of HebeiChina

Personalised recommendations