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Multimedia Systems

, Volume 16, Issue 4–5, pp 231–241 | Cite as

A hybrid similarity measure of contents for TV personalization

  • Zhiwen Yu
  • Xingshe Zhou
  • Liang Zhou
  • Kejun Du
Regular Paper

Abstract

Similarity measure of contents plays an important role in TV personalization, e.g., TV content group recommendation and similar TV content retrieval, which essentially are content clustering and example-based retrieval. We define similar TV contents to be those with similar semantic information, e.g., plot, background, genre, etc. Several similarity measure methods, notably vector space model based and category hierarchy model based similarity measure schemes, have been proposed for the purpose of data clustering and example-based retrieval. Each method has advantages and shortcomings of its own in TV content similarity measure. In this paper, we propose a hybrid approach for TV content similarity measure, which combines both vector space model and category hierarchy model. The hybrid measure proposed here makes the most of TV metadata information and takes advantage of the two similarity measurements. It measures TV content similarity from the semantic level other than the physical level. Furthermore, we propose an adaptive strategy for setting the combination parameters. The experimental results showed that using the hybrid similarity measure proposed here is superior to using either alone for TV content clustering and example-based retrieval.

Keywords

Similarity measure Clustering Example-based retrieval TV-Anytime Vector space model Category hierarchy model 

Notes

Acknowledgments

This work was partially supported by the National Natural Science Foundation of China (No. 60903125), the High-Tech Program of China (863) (No. 2009AA011903), and the Program for New Century Excellent Talents in University (No. NCET-09-0079).

References

  1. 1.
    TV-Anytime requirements on environment, TV-Anytime forum, TV035r6, 2000Google Scholar
  2. 2.
    Yu, Z., Zhou, X.: TV3P: An Adaptive Assistant for Personalized TV. IEEE Trans Consum Electron 50(1), 393–399 (2004)CrossRefGoogle Scholar
  3. 3.
    Hong C., Lim J.: Design and Implementation of Home Media Server Using TV-Anytime for Personalized Broadcasting Service. In: International conference on computational science and its applications (ICCSA 2005), LNCS 3483, pp. 138–147, Springer Press, 9–12 May, Singapore (2005)Google Scholar
  4. 4.
    López A., Fabregat J., Rovira Vall, M., Mas J., Fernàndez, G.: IndexTV: a TV-Anytime-based personalized recommendation system for digital TV. In: SPIE conference on multimedia systems and applications, vol. 2, pp. 40–50, October 25–28, Philadelphia, Pennsylvania, USA (2004)Google Scholar
  5. 5.
    Kazasis F.G., Moumoutzis N., Pappas N., Karanastasi A., Christodoulakis S.: Designing ubiquitous personalized TV-Anytime services. In: Ubiquitous mobile information and collaboration systems (UMICS 2003), 16–17 June, Klagenfurt/Velden, Austria (2003)Google Scholar
  6. 6.
    Yu Z., Zhou X., Hao Y., Gu J.: TV program recommendation for multiple viewers based on user profile merging. In: User modeling and user-adapted interaction, Vol. 16, No. 1, Springer Press, March, pp. 63–82 (2006)Google Scholar
  7. 7.
    Zhikai Z., Guangfan Z., Huihe S.: Data mining and knowledge discovery: an overview and prospect. In: Information and control, pp. 357–365, 1999Google Scholar
  8. 8.
    Chang N.S., Fu K.S.: Query by pictorial example. IEEE. Trans. Softw. Eng. 6(6), 519–524 (1980)CrossRefGoogle Scholar
  9. 9.
    Salton, G.: Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley, Massachusetts, USA (1989)Google Scholar
  10. 10.
    Sowa, J.F.: Conceptual Structures. Addison-Wesley, Reading, MA (1984)zbMATHGoogle Scholar
  11. 11.
    Hoi, C.H., Wang, W., Lyu, M.: A Novel Scheme for Video Similarity Detection. In: International conference on image and video retrieval (CIVR 2003). Lecture Notes in Computer Science, vol. 2728, pp. 358–367Google Scholar
  12. 12.
    Cheung, S.C., Zakhor, A.: Efficient video similarity measurement with video signature. IEEE Trans Circuits Syst Video Technol 13(1), 59–74 (2003)CrossRefGoogle Scholar
  13. 13.
    Cheung S-C., Zakhor A.: Efficient video similarity measurement and search. In: The 7th ieee international conference on image processing (ICIP 2000), pages 85–88, Vancouver, British Columbia, September 2000Google Scholar
  14. 14.
    Lin T., Ngo, C., Zhang, H., Shi, Q.: Integrating color and spatial features for content-based video retrieval. In: The 8th IEEE international conference on image processing (ICIP 2001), Thessaloniki, Greece, 7–10 October 2001Google Scholar
  15. 15.
    Iyengar, G., Lippman A.: Evolving discriminators for querying video sequences. In: Proceedings of 1997 SPIE storage and retrieval for image and video databases, San Jose, California, February 1997, pp. 154–165Google Scholar
  16. 16.
    Ardizzone, E., La Cascia, M.: Automatic video database indexing and retrieval. Multimed Tools Appl 4(1), 29–56 (1997)CrossRefGoogle Scholar
  17. 17.
    Abdel-Mottaleb, M., Dimitrova, N.: CONIVAS: content-based image and video access system. In: Proceedings of the fourth ACM international conference on multimedia, Nov 1996, pp. 427–428Google Scholar
  18. 18.
    Lee, D.J., Antani, S., Long, L.R.: Similarity measurement using polygon curve representation and Fourier descriptors for shape-based vertebral image retrieval. SPIE Med Imaging Image Process 5032, 1283–1291 (2003)Google Scholar
  19. 19.
    Doulamis N., Doulamis A.: Optimal recursive similarity measure estimation for interactive content-based image retrieval. In: The 9th IEEE international conference on image processing (ICIP 2002), Vol. 1, pp. 972–975Google Scholar
  20. 20.
    Niblack, W., et al.: The QBIC project: querying images by content using color, texture and shape. In: Proceedings of symposium on electronic imaging science and technology: storage and retrieval for image video databases, SPIE, V1908, San Jose, CA, pp. 173–187 (1993)Google Scholar
  21. 21.
    Manjunath B.S., Ma W.Y.: Texture features for browsing and retrieval of large image data. In: IEEE trans pattern analysis and machine intelligence, special issue on digital libraries, Nov. 1996, pp. 837–842Google Scholar
  22. 22.
    Smith, J., Chang, S.-F.: VisualSEEk: a fully automated content-based image query system. In: Proceedings of ACM Multimedia’96, Nov. 1996, pp. 87–98Google Scholar
  23. 23.
    Jeh, G., Widom, J.: SimRank: a measure of structural-context similarity. In: Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining, pp. 538–543, Edmonton, Alberta, Canada, July 2002Google Scholar
  24. 24.
    Baeza-Yates, R., Ribeiro-Neto B.: Modern Information Retrieval. Addison Wesley, Reading, MA (1999)Google Scholar
  25. 25.
    Salton, G., McGill, M.J.: Introduction to modern information retrieval. McGraw–Hill, New York (1983)zbMATHGoogle Scholar
  26. 26.
    Hirakawa, H., Xu, Z., Haase, K.: Inherited feature-based similarity measure based on large semantic hierarchy and large text corpus. In: Proceedings of the 16th conference on computational linguistics, pp. 508–513, Copenhagen, Denmark (1996)Google Scholar
  27. 27.
    Ge, J., Qiu, Y.: Concept similarity matching based on semantic distance. In: Proceedings of the fourth international conference on semantics, knowledge and grid, 2008, pp. 380–383Google Scholar
  28. 28.
    Yu, Z., Nakamura, Y., Zhang, D., Kajita, S., Mase, K.: Content provisioning for ubiquitous learning. IEEE Pervasive Comput 7(4), 62–70 (2008)CrossRefGoogle Scholar
  29. 29.
    Wen, J.R., Nie, J.Y., Zhang, H.J.: Query clustering using user logs. ACM Trans Inf Syst 20(1), 59–81 (2002)CrossRefGoogle Scholar
  30. 30.
    Bray, T., Paoli, J., Sperberg-McQueen, C.M.: Extensible markup language (XML) 1.0, http://www.w3.org/TR/REC-xml (2000)
  31. 31.
    TV-Anytime System Description Document (Informative with mandatory appendix B), WD608, TV-Anytime Forum, 2002Google Scholar
  32. 32.
    TV-Anytime metadata specifications document, SP003v12 Part A AppendixB, TV-Anytime Forum, 2002Google Scholar
  33. 33.
    ISO 8601: Data elements and interchange formats—Information interchange—Representation of dates and times, International Organization for Standardization, 2004Google Scholar
  34. 34.
    Yan, T.,W., Garcia-Molina, H.: Index structures for information filtering under the vector space model. In: Proceedings of the tenth international conference on data engineering, Houston, USA, 1994Google Scholar
  35. 35.
    The internet movie database. Available at http://imdb.com/ (2005)
  36. 36.
    Gowda, K.C., Diday, E.: Symbolic clustering using a new similarity measure. IEEE Trans Syst Man Cybern 20, 368–377 (1992)CrossRefGoogle Scholar
  37. 37.
    Rijsbergen, C.J.: Information Retrieval, 2nd edn. Butterworths, London (1979)Google Scholar
  38. 38.
    Konstan, J., Miller, B., Maltz, D., et al.: GroupLens: applying collaborative filtering to usenet news. Commun ACM 40(3), 77–87 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.School of Computer ScienceNorthwestern Polytechnical UniversityXi’anPeople’s Republic of China
  2. 2.Lab of UEIENSTA-ParisTechParisFrance

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