Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Personalized Multimedia Search

  • Chapter
  • First Online:
User-centric Social Multimedia Computing

Part of the book series: Springer Theses ((Springer Theses))

  • 443 Accesses

Abstract

Given results from multimedia content analysis and user modeling, personalized multimedia services are developed to satisfy customized needs. In this chapter, we introduce a general solution framework for personalized multimedia search. We first propose a multimodal generative model to simultaneously address tasks of multimedia content analysis and user modeling, and then present the risk minimization-based theoretical framework for personalized image search. The framework considers the noisy tag issue and enables easy incorporation of social relation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    We use topic distributions over tag words and visual descriptors to represent the topic space: \(T\) is the number of topics, \(|\fancyscript{W}|\) is the size of tag vocabulary, and \(|\fancyscript{V}|\) is the size of visual-descriptor vocabulary.

  2. 2.

    \(\varPsi _{u_1,u_2}(t), u_2\in \fancyscript{C}_{u_1}\) measures the influence strength from user \(u_2\) to \(u_1\) in the \(t\)th topic.

  3. 3.

    We assume that visual descriptor prior and tag word prior all follow uniform distribution, i.e., \(p(v_{di})=\frac{1}{|\fancyscript{V}|}\), \(p(w_{di})=\frac{1}{|\fancyscript{W}|}\).

References

  1. Anagnostopoulos, A., Kumar, R., Mahdian, M.: Influence and correlation in social networks. In: KDD, pp. 7–15 (2008)

    Google Scholar 

  2. Bender, M., Crecelius, T., Kacimi, M., Michel, S., Neumann, T., Parreira, J.X., Schenkel, R., Weikum, G.: Exploiting social relations for query expansion and result ranking. In: ICDE Workshops, pp. 501–506 (2008)

    Google Scholar 

  3. Blei, D.M., Jordan, M.I.: Modeling annotated data. In: SIGIR, pp. 127–134 (2003)

    Google Scholar 

  4. Carmel, D., Zwerdling, N., Guy, I., Ofek-Koifman, S., Har’El, N., Ronen, I., Uziel, E., Yogev, S., Chernov, S.: Personalized social search based on the user’s social network. In: CIKM, pp. 1227–1236 (2009)

    Google Scholar 

  5. Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: KDD, pp. 1029–1038 (2010)

    Google Scholar 

  6. Chirita, P.-A., Nejdl, W., Paiu, R., Kohlschütter, C.: Using odp metadata to personalize search. In: SIGIR, pp. 178–185 (2005)

    Google Scholar 

  7. Chua, T.-S., Tang, J., Hong, R., Li, H., Luo, Z., Zheng, Y.: Nus-wide: a real-world web image database from National University of Singapore. In: CIVR (2009)

    Google Scholar 

  8. Cialdini, R.B.: Influence: The Psychology of Persuasion. HarperCollins, New York (2009)

    Google Scholar 

  9. Crandall, D.J., Cosley, D., Huttenlocher, D.P., Kleinberg, J.M., Suri, S.: Feedback effects between similarity and social influence in online communities. In: KDD, pp. 160–168 (2008)

    Google Scholar 

  10. Easley, D., Kleinberg, J.: Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, Cambridge (2010)

    Book  Google Scholar 

  11. Gou, L., Chen, H.-H., Kim, J.-H., Zhang, X., Giles, C.L.: Sndocrank: a social network-based video search ranking framework. In: Multimedia, Information Retrieval, pp. 367–376 (2010)

    Google Scholar 

  12. Kempe, D., Kleinberg, J.M., Tardos, É.: Maximizing the spread of influence through a social network. In: KDD, pp. 137–146 (2003)

    Google Scholar 

  13. Kraft, R., Maghoul, F., Chang, C.-C.: Y!q: contextual search at the point of inspiration. In: CIKM, pp. 816–823 (2005)

    Google Scholar 

  14. Lafferty, J.D., Zhai, C.: Document language models, query models, and risk minimization for information retrieval. In: SIGIR, pp. 111–119 (2001)

    Google Scholar 

  15. Lane, N.D., Lymberopoulos, D., Zhao, F., Campbell, A.T.: Hapori: context-based local search for mobile phones using community behavioral modeling and similarity. In: Proceedings of the UbiComp, pp. 109–118 (2010)

    Google Scholar 

  16. Lerman, K.: Handbook of Research on Web 2.0, 3.0, and X.0: Technologies, Business, and Social Applications, Chapter Leveraging User-specified Metadata to Personalize Image Search. IGI Global, Pennsylvania (2009)

    Google Scholar 

  17. Liu, L., Tang, J., Han, J., Jiang, M., Yang, S.: Mining topic-level influence in heterogeneous networks. In: CIKM, pp. 199–208 (2010)

    Google Scholar 

  18. Lu, D., Li, Q.: Personalized search on flickr based on searcher’s preference prediction. In: WWW (Companion Volume), pp. 81–82 (2011)

    Google Scholar 

  19. Lu, D., Li, Q., Liao, S.: A graph-based action network framework to identify prestigious members through member’s prestige evolution. Decis. Support Syst. 53(1), 44–54 (2012)

    Article  Google Scholar 

  20. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)

    Article  Google Scholar 

  21. Pang, L., Cao, J., Zhang, Y., Lin, S.: Leveraging collective wisdom for web video retrieval through heterogeneous community discovery. In: ACM Multimedia, pp. 1485–1488 (2011)

    Google Scholar 

  22. Ponte, J.M., Croft, W.B.: A language modeling approach to information retrieval. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 275–281 (1998)

    Google Scholar 

  23. Qiu, F., Cho, J.: Automatic identification of user interest for personalized search. In: WWW, pp. 727–736 (2006)

    Google Scholar 

  24. Robert, C.P., Casella, G.: Monte Carlo Statistical Methods. Springer, New York (1999)

    Book  MATH  Google Scholar 

  25. Ronald, N., Dignum, V., Jonker, C.M.: When will i see you again: modelling the influence of social networks on social activities. In: MALLOW (2010)

    Google Scholar 

  26. Sang, J., Xu, C.: Right buddy makes the difference: an early exploration of social relation analysis in multimedia applications. In: ACM Multimedia, pp. 19–28 (2012). http://dl.acm.org/10.1145/2393347.2393358

  27. Sang, J., Xu, C.: Social influence analysis and application on multimedia sharing websites. TOMCCAP, 9(1s) (2013). http://dl.acm.org/10.1145/2502436

  28. Sang, J., Xu, C., Liu, J.: User-aware image tag refinement via ternary semantic analysis. IEEE Trans. Multimed. 14(3), 883–895 (2012)

    Article  Google Scholar 

  29. Smyth, B.: A community-based approach to personalizing web search. Computer 40(8), 42–50 (2007)

    Article  Google Scholar 

  30. Tang, J., Sun, J., Wang, C., Yang, Z.: Social influence analysis in large-scale networks. In: KDD, pp. 807–816 (2009)

    Google Scholar 

  31. Xiang, R., Neville, J., Rogati, M.: Modeling relationship strength in online social networks. In: WWW, pp. 981–990 (2010)

    Google Scholar 

  32. Zhuang, J., Mei, T., Hoi, S.C.H., Hua, X.-S., Li, S.: Modeling social strength in social media community via kernel-based learning. In: ACM Multimedia, pp. 113–122 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Jitao Sang

Authors
  1. Jitao Sang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jitao Sang .

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Sang, J. (2014). Personalized Multimedia Search. In: User-centric Social Multimedia Computing. Springer Theses. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44671-3_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-44671-3_4

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44670-6

  • Online ISBN: 978-3-662-44671-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation