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Probabilistic Approaches to Tag Recommendation in a Social Bookmarking Network

  • Oly Mistry
  • Sandip Sen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7057)

Abstract

Tagging has become increasingly popular with the explosion of user-created content on the web. A ‘tag’ can be defined as a group of keywords that makes organizing, browsing and searching for content more efficient. Users apply tags to a variety of web-based, shareable content including photos, videos, news articles, bookmarks, friends, etc. Tag suggestions for blog posts or web-pages have changed the focus of the tagging process from generation to recognition, thus making it less time and effort intensive. We propose tag recommendation algorithms for personalized agents, that recommend tags for bookmarks stored in a popular social bookmarking website, Del.ici.ous [6]. Our tag recommender agents learn to classify the tags according to their semantic similarity based on collaborative tagging by the users. Hence this approach can be used to facilitate folksonomy formation for the social network. In this paper, we first empirically verify our hypothesis that web pages with similar content are tagged with similar tags. We compare both Content-based and Collaborative approaches to recommend tags to the users. We analyze the performance of two probabilistic approaches to recommend tags from users with similar tagging behavior.

Keywords

Collaborative Recommendation Content Base Recommendation System Poisson Mixture Model Position Base System Propose Recommendation Approach 
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.
    P. S. Algorithm. Stemming algorithm, http://en.wikipedia.org/wiki/Stemming
  2. 2.
    T. API. Technorati was founded to help bloggers succeed by collecting, highlighting, and distributing the global online conversation, http://technorati.com/
  3. 3.
    Basu, C., Hirsh, H., Cohen, W.W.: Recommendation as classification: Using social and content-based information in recommendation. In: AAAI/IAAI, pp. 714–720 (1998)Google Scholar
  4. 4.
    I.R. .M.L.D.D.-L. Berlin Institute of Technology. Dai labor, berlin, http://www.dai-labor.de/en/competence_centers/irml/datasets/
  5. 5.
    T. Cloud. Delicous, http://delicious.com/tag
  6. 6.
    Delicious. Social bookmarking website, http://www.delicious.com
  7. 7.
    Fellbaum, C.: WordNet: An Electronical Lexical Database. The MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  8. 8.
    Goldstein, D.G., Gigerenzer, G.: The recognition heuristic: How ignorance makes us smart. In: Gigerenzer, G., Todd, P.M., The ABC Research Group (eds.) Simple Heuristics That Make Us Smart, ch. 2, pp. 37–58. Oxford University Press, New York (1999)Google Scholar
  9. 9.
    Golub, G.H., Van Loan, C.F.: Matrix Computations (Johns Hopkins Studies in Mathematical Sciences). The Johns Hopkins University Press (October 1996)Google Scholar
  10. 10.
    Li, J., Zha, H.: Two-way poisson mixture models for simultaneous document classification and word clustering. Computational Statistics & Data Analysis 50(1), 163–180 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Liang, H., Xu, Y., Li, Y., Nayak, R.: Collaborative filtering recommender systems using tag information. In: Web Intelligence/IAT Workshops, pp. 59–62. IEEE (2008)Google Scholar
  12. 12.
    Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval, 1st edn. Cambridge University Press (July 2008)Google Scholar
  13. 13.
    Mitchell, T.M.: Machine Learning. McGraw-Hill Science/Engineering/Math (March 1997)Google Scholar
  14. 14.
    Sigma, Andy: Automatic tag recommendation for the web 2.0 blogosphere using collaborative tagging and hybrid ann semantic structures. In: ACOS 2007, pp. 88–93. WSEAS, Stevens Point (2007)Google Scholar
  15. 15.
    Song, Y.: Automatic tag recommendation algorithms for social recommender systems - microsoft research. ACM Transactions on Web (2009)Google Scholar
  16. 16.
    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. ACM, New York (2008)Google Scholar
  17. 17.
    Sood, S., Owsley, S., Hammond, K., Birnbaum, L.: Tagassist: Automatic tag suggestion for blog postsGoogle Scholar
  18. 18.
    Tso Sutter, K.H.L., Marinho, L.B., Thieme, L.S.: Tag-aware recommender systems by fusion of collaborative filtering algorithms. In: SAC 2008, pp. 1995–1999. ACM, New York (2008)Google Scholar
  19. 19.
    Wal, T.V.: Folksonomy definition and wikipedia :: Off the top :: vanderwal.net.Google Scholar
  20. 20.
    Wang, J., de Vries, A.P., Reinders, M.J.T.: Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: SIGIR 2006, pp. 501–508. ACM Press, New York (2006)Google Scholar
  21. 21.
    Xu, Z., Fu, Y., Mao, J., Su, D.: Towards the semantic web: Collaborative tag suggestions. In: WWW 2006: Proceedings of the Collaborative Web Tagging Workshop, Edinburgh, Scotland (2006) æGoogle Scholar
  22. 22.
    Zha, H., He, X., Ding, C., Simon, H., Gu, M.: Bipartite graph partitioning and data clustering. In: CIKM 2001, pp. 25–32. ACM Press, New York (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Oly Mistry
    • 1
  • Sandip Sen
    • 1
  1. 1.University of TulsaTulsaUSA

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