Cross-category Recommendation for Multimedia Content

  • Naoki Kamimaeda
  • Tomohiro Tsunoda
  • Masaaki Hoshino


The purpose of this article is to introduce cross-category recommendation technologies for multimedia content. First, in order to understand how to realize the recommendation function, multimedia content recommendation technologies and cross-category recommendation technologies are outlined. Second, practical applications and services using these technologies are described. Finally, difficulties involving cross-category recommendation for multimedia content and future prospects are mentioned as the conclusion.


Recommendation System User Preference Collaborative Filter Vector Space Model Content Profile 
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.



We would like to thank our colleagues at the PAO Gp., Intelligent Systems Research Laboratory, System Technologies Laboratories, Corporate R&D, Sony Corporation and Sec.5, Intelligence Application Development Dept., Common Technology Division, Technology Development Group, Corporate R&D, Sony Corporation for their invaluable assistance.


  1. 1.
    NetCraft “January 2009 Web Server Survey.”
  2. 2.
    P. Resnick et al. (1994). GroupLens: An Open Architecture for Collaborative Filtering of Netnews. Proc. ACM 1994 Conf. Computer Supported Cooperative Work, ACM Press, pp. 175–186.Google Scholar
  3. 3.
    G. Linden, B. Smith, and J. York (2003). Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing: Industry Report.
  4. 4.
  5. 5.
    MusicStrands (MyStrands).
  6. 6.
  7. 7.
    George Chang, Marcus J. Healey, James A.M. McHugh, Jason T.L. Wang, “Mining the World Wide Web”, Kluwer Academic Publishers, 2001.Google Scholar
  8. 8.
    Jeongphee Yi, Tetsuya Nasukawa, Razvan Bunescu, Wayne Niblack, “Sentiment Analyzer: Extracting Sentiments about a Given Topic Using Natural Language Processing Techniques”, ICDM 2003.Google Scholar
  9. 9.
  10. 10.
    Pandora Internet Radio.
  11. 11.
  12. 12.
    TV Kingdom. (in Japanese)
  13. 13.
    T. Tsunoda, M. Hoshino, “Automatic Metadata Expansion and Indirect Collaborative Filtering for TV Program Recommendation System”, Euro ITV 2006.Google Scholar
  14. 14.
  15. 15.
    Google Page Rank technology.
  16. 16.
    Milan Petkovic, Willem Jonker, “Content-Based Video Retrieval”, Kluwer Academic Publishers, 2002.Google Scholar
  17. 17.
    M. Flickner et al., “Query by Image and Video. Content: The QBIC System”, IEEE Computer, Vol. 28, No. 9, pp. 23–32, 1995.Google Scholar
  18. 18.
    J. R. Smith, S.-F. Chang, “VisualSEEk: A Fully Automated Content-Based Image Query System”, ACM Multimedia, 1996, pp. 87–98.Google Scholar
  19. 19.
    Yossi Rubner, Leonidas J. Guibas, Carlo Tomasi, “The Earth Mover’s Distance, Multi-Dimensional Scaling, and Color-Based Image Retrieval”, in Proceedings of the ARPA Image Understanding Workshop, New Orleans, LA, May 1997, pp. 661–668.Google Scholar
  20. 20.
    JSEG: Color Image Segmentation.
  21. 21.
    David G. Lowe, “Distinctive Image Features from Scale Invariant Keypoints”, International Journal of Computer Vision, Vol. 60, No. 2, pp. 91–110, 2004.CrossRefGoogle Scholar
  22. 22.
  23. 23.
    Aymeric Zils, Francois Pachet, “Automatic Extraction of Music Descriptors from Acoustic Signals Using EDS”, in Proceedings of the 116th AES Convention, May 2004.Google Scholar
  24. 24.
    Sony’s Hard-drive-based Music Systems “Giga Juke.”
  25. 25.
    P. Cano, E. Battle, T. Kalker, J. Haitsma, “A Review of Algorithm for Audio Fingerprinting”, in Workshop on Multimedia Signal Processing, 2002.Google Scholar
  26. 26.
  27. 27.
  28. 28.
    Brian Clarkson, Alex Pentland, “Unsupervised Clustering of Ambulation Audio and Video”, ICASSP 98.Google Scholar
  29. 29.
    Tom M. Mitchell, “Machine Learning”, WCB/McGraw-Hill, 1997, pp. 177–184.Google Scholar
  30. 30.
    N. Yamamoto, M. Saito, M. Miyazaki, H. Koike, “Recommendation Algorithm Focused on Individual Viewpoints”, IEEE CCNC 2005 pp. 65–70, 2005.Google Scholar
  31. 31.
  32. 32.
  33. 33.
    Sony Marketing (Japan) Inc. (2009). branco home page (in Japanese)
  34. 34.
  35. 35.
    Sony Ericsson Mobile Communications AB.
  36. 36.
  37. 37.
    Sony Corporation (2008). VAIO Giga Pocket Digital homepage (in Japanese)

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Naoki Kamimaeda
    • 1
  • Tomohiro Tsunoda
    • 1
  • Masaaki Hoshino
    • 1
  1. 1.Sec. 5, Intelligence Application Development Dept., Common Technology Division, Technology Development Group, Corporate R&DSony CorporationTokyoJapan

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