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Tag Recommendation for Flickr Using Web Browsing Behavior

  • Taiki Takashita
  • Tsuyoshi Itokawa
  • Teruaki Kitasuka
  • Masayoshi Aritsugi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6017)

Abstract

It is fun to share photos with other people easily and effectively. For it, a tag recommendation system for Flickr is developed in this paper. We assume that there are some relations between a photo of which a user attempts to update tags and webpages that the user has browsed. In our system, Web browsing behavior of a user is exploited to suggest not only candidate tags to be added to but also candidate tags to be deleted from a photo in Flickr to the user. We discuss how to implement the system in this paper. We also report some experimental results to show the effectiveness of our system.

Keywords

Image Annotation Relevancy Score Inverse Document Frequency Mean Reciprocal Rank Multiple Bernoulli Relevance Model 
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.
    Budzik, J., Hammond, K.J.: User interactions with everyday applications as context for just-in-time information access. In: IUI 2000. In: Proceedings of the 5th international conference on Intelligent user interfaces, pp. 44–51. ACM, New York (2000)CrossRefGoogle Scholar
  2. 2.
    Cilibrasi, R., Vitanyi, P.: Automatic extraction of meaning from the web. In: Proc. IEEE International Symposium on Information Theory, July 2006, pp. 2309–2313 (2006)Google Scholar
  3. 3.
    Feng, S.L., Manmatha, R., Lavrenko, V.: Multiple bernoulli relevance models for image and video annotation. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1002–1009. IEEE Computer Society Press, Los Alamitos (2004)Google Scholar
  4. 4.
  5. 5.
  6. 6.
    Garg, N., Weber, I.: Personalized, interactive tag recommendation for flickr. In: RecSys 2008: Proceedings of the 2008 ACM conference on Recommender systems, pp. 67–74. ACM, New York (2008)CrossRefGoogle Scholar
  7. 7.
    Jeon, J., Lavrenko, V., Manmatha, R.: Automatic image annotation and retrieval using cross-media relevance models. In: SIGIR 2003: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pp. 119–126. ACM, New York (2003)CrossRefGoogle Scholar
  8. 8.
    Lavrenko, V., Manmatha, R., Jeon, J.: A model for learning the semantics of pictures. In: Neural Information Processing Systems, NIPS (2003), http://books.nips.cc/papers/files/nips16/NIPS2003_AA70.pdf
  9. 9.
    Liu, J., Wang, B., Li, M., Li, Z., Ma, W., Lu, H., Ma, S.: Dual cross-media relevance model for image annotation. In: MULTIMEDIA 2007: Proceedings of the 15th international conference on Multimedia, pp. 605–614. ACM, New York (2007)CrossRefGoogle Scholar
  10. 10.
    Mathes, A.: Folksonomies – cooperative classification and communication through shared metadata. Tech. rep., Computer Mediated Communication (LIS590CMC), University of Illinois, Urbana-Champaign (December 2004), http://www.adammathes.com/academic/computer-mediated-communication/folksonomies.html
  11. 11.
    Peterson, E.: Beneath the metadata – some philosophical problems with folksonomy. D-Lib. Magazine 12(11) (November 2006), http://dx.doi.org/10.1045/november2006-peterson
  12. 12.
    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 Press, New York (2008)CrossRefGoogle Scholar
  13. 13.
    Sigurbjörnsson, B., van Zwol, R.: Flickr tag recommendation based on collective knowledge. In: WWW 2008: Proceeding of the 17th international conference on World Wide Web, pp. 327–336. ACM Press, New York (2008)CrossRefGoogle Scholar
  14. 14.
    Takashita, T., Itokawa, T., Kitasuka, T., Aritsugi, M.: Extracting user preference from Web browsing behaviour for spam filtering. Int. J. Adv. Intell. Paradigms 1(2), 126–138 (2008)CrossRefGoogle Scholar
  15. 15.
    Takashita, T., Itokawa, T., Kitasuka, T., Aritsugi, M.: A spam filtering method learning from Web browsing behavior. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008, Part II. LNCS (LNAI), vol. 5178, pp. 774–781. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
  17. 17.
    Yahoo! Search Web Services: http://developer.yahoo.com/search/

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Taiki Takashita
    • 1
  • Tsuyoshi Itokawa
    • 1
  • Teruaki Kitasuka
    • 1
  • Masayoshi Aritsugi
    • 1
  1. 1.Computer Science and Electrical EngineeringGraduate School of Science and Technology Kumamoto UniversityKumamotoJapan

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