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Efficient Recommendation for Smart TV Contents

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNISA,volume 7678)

Abstract

In this paper, we propose an efficient recommendation technique for smart TV contents. Our method solves the scalability and sparsity problems from which the conventional algorithms suffer in smart TV environment characterized by the large numbers of users and contents. Our method clusters users into user groups of similar preference patterns and a set of similar users to the target user are extracted, and then the user-based collaborative filtering is applied. We experimented with our method using the data of the real one-month IPTV services. The experiment results showed the success rate of 93.6% and the precision of 77.4%, which are recognized as a good performance for smart TV. We also investigate integration of recommendation methods for more personalized and efficient recommendation. Category match ratios for different integrations are compared as a measure for personalized recommendation.

Keywords

  • recommendation
  • collaborative filtering
  • clustering
  • smart TV
  • data mining
  • ISOData

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References

  1. Kim, K.H., Ahn, C.H., Hong, J.W.: Research and Standardization Trends on Smart TV. Electronics and Telecommunications Trends, 37–49 (2011)

    Google Scholar 

  2. Melville, P., Sindhwani, V.: Recommender Systems. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 829–838. Springer US (2010)

    Google Scholar 

  3. Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)

    CrossRef  Google Scholar 

  4. Kim, M.W., Kim, E.-J.: Performance Improvement in Collaborative Recommendation Using Multi-Layer Perceptron. In: King, I., Wang, J., Chan, L.-W., Wang, D. (eds.) ICONIP 2006. LNCS, vol. 4234, pp. 350–359. Springer, Heidelberg (2006)

    CrossRef  Google Scholar 

  5. Varshavsky, R., Tennenholtz, M., Karidi, R.: Hybrid Recommendation System. Google Patents (2009)

    Google Scholar 

  6. Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining Content-based and Collaborative Filters in an Online Newspaper. In: Proceedings of ACM SIGIR Workshop on Recommender Systems (1999)

    Google Scholar 

  7. Kim, B.M., Li, Q.: A Hybrid Recommendation Method based on Attributes of Items and Ratings. Journal of KIISE: Software and Applications 31(12), 1672–1683 (2004)

    Google Scholar 

  8. Linden, G., Smith, B., York, J.: Amazon.com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing 7(1), 76–80 (2003)

    CrossRef  Google Scholar 

  9. Lowd, D., Godde, O., McLaughlin, M., Nong, S., Wang, Y., Herlocker, J.L.: Challenges and Solutions for Synthesis of Knowledge Regarding Collaborative Filtering Algorithms. Technical Reports (Electrical Engineering and Computer Science), Oregon State University (2009)

    Google Scholar 

  10. Xue, G.R., Lin, C., Yang, Q., Xi, W.S., Zeng, H.J., Yu, Y., Chen, Z.: Scalable collaborative filtering using cluster-based smoothing. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 114–121. ACM (2005)

    Google Scholar 

  11. Al Mamunur Rashid, S.K.L., Karypis, G., Riedl, J.: ClustKNN: a highly scalable hybrid model-& memory-based CF algorithm. In: Proceeding of WebKDD 2006. ACM (2006)

    Google Scholar 

  12. Jung, H.Y., Kim, M.S.: Collaborative Filtering Model Analysis based on IPTV Viewing Log. In: Proc. of the 37th KIISE Fall Conference, KIISE, vol. 37(1(C)), pp. 404–409 (2010)

    Google Scholar 

  13. Kim, E.J., Song, W.M., Song, S.Y., Kim, M.W.: An Efficient Collaborative Recommendation Technique for IPTV Services. Journal of KIISE: Software and Applications 39(5), 390–398 (2012)

    Google Scholar 

  14. Manning, C.D., Raghavan, P., Schutze, H.: Introduction to Information Retrieval, vol. 1. Cambridge University Press, Cambridge (2008)

    CrossRef  MATH  Google Scholar 

  15. Memarsadeghi, N., Mount, D.M., Netanyahu, N.S., Le Moigne, J., de Berg, M.: A Fast Implementation of the ISODATA Clustering Algorithm. International Journal of Computational Geometry and Applications 17(1), 71–103 (2007)

    CrossRef  MathSciNet  MATH  Google Scholar 

  16. Lin, W., Alvarez, S.A., Ruiz, C.: Collaborative Recommendation via Adaptive Association Rule Mining. Data Mining and Knowledge Discovery 6(1), 83–105 (2002)

    CrossRef  MathSciNet  Google Scholar 

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Kim, MW., Kim, EJ., Song, WM., Song, SY., Ra Khil, A. (2012). Efficient Recommendation for Smart TV Contents. In: Srinivasa, S., Bhatnagar, V. (eds) Big Data Analytics. BDA 2012. Lecture Notes in Computer Science, vol 7678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35542-4_14

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  • DOI: https://doi.org/10.1007/978-3-642-35542-4_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35541-7

  • Online ISBN: 978-3-642-35542-4

  • eBook Packages: Computer ScienceComputer Science (R0)