Designing a Hybrid Recommendation System for TV Content

Chapter
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 42)

Abstract

In the area of knowledge-based information systems, TV recommendation systems have attracted researchers’ attention with the development of Smart TV and expansion of TV content . Traditional TV recommendation systems, which are based on individual’s viewing activity and only recommending TV programs, have been unable to meet the requirements of Smart TV. Hence, this chapter proposes a hybrid recommendation system, which not only uses the information of single user’s activity, but also takes into account other users’ viewing habits and related information from the Internet, for different content such as TV programs, movies, and music. The proposed recommendation system integrates Content Analysis Component, User Analysis Component and Preference Learning Component. Moreover, this chapter also discusses several important design issues, such as diversity, novelty, explanation and group recommendations, which should be considered in designing/building a TV recommendation system. The proposed framework could be used to help designers and developers to design a TV recommendation system engine for Smart TV.

Keywords

TV content Recommendation systems Smart TV 

References

  1. 1.
    Das, D., ter Horst, H.: Recommender systems for TV. Technical Report WS-98-08, AAAI, California, USA (1998)Google Scholar
  2. 2.
    Asabere, N.Y.: A survey of personalized television and video recommender systems and techniques. Int. J. Inf. Commun. Technol. Res. 2(7), 602–608 (2012)Google Scholar
  3. 3.
    Ardissono, L., Gena, C., Torasso, P., Bellifemine, F., Difino, A., Negro, B.: User modeling and recommendation techniques for personalized electronic program guides. In: Personalized Digital Television. Human-Computer Interaction Series, vol. 6, pp. 3–26. Springer, Netherlands (2004)Google Scholar
  4. 4.
    Cotter, P., Smyth, B.: Ptv: Intelligent personalised tv guides. In: Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence, AAAI Press (2000), pp. 957–964Google Scholar
  5. 5.
    Engelbert, B., Blanken, M., Kruthoff-Bruwer, R., Morisse, K.: A user supporting personal video recorder by implementing a generic bayesian classifier based recommendation system. In: 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops). (March 2011), pp. 567–571Google Scholar
  6. 6.
    Hsu, S.H., Wen, M.H., Lin, H.C., Lee, C.C., Lee, C.H.: Aimed- a personalized tv recommendation system. In: Cesar, P., Chorianopoulos, K., Jensen, J. (eds.) Interactive TV: a Shared Experience. Lecture Notes in Computer Science, vol. 4471, pp. 166–174. Springer, Berlin (2007)CrossRefGoogle Scholar
  7. 7.
    Martínez, A.: Pazos Arias, J.J., Vilas, A.F., Duque, J.G., Nores, M.L.: What’s on tv tonight? an efficient and effective personalized recommender system of tv programs. IEEE Trans. Consum. Electron. 55(1), 286–294 (2009)Google Scholar
  8. 8.
    Smyth, B., Cotter, P.: A personalised tv listings service for the digital tv age. Knowl. Based Syst. 13(2–3), 53–59 (2000)CrossRefGoogle Scholar
  9. 9.
    Srinivas, K.K., Gutta, S., Schaffer, D., Martino, J., Zimmerman, J.: A multi-agent tv recommender. In: Workshop on Personalization in Future TV, Sonthofen (2001)Google Scholar
  10. 10.
    Uberall, C., Muttukrishnan, R., Rakocevic, V., Jager, R., Kohnen, C.: Recommendation index for dvb content using service information. In: Proceedings of the 2009 IEEE International Conference on Multimedia and Expo, pp. 1178–1181. IEEE Press, Piscataway, NJ, USA (2009)Google Scholar
  11. 11.
    Yu, Z., Zhou, X., Hao, Y., Gu, J.: Tv program recommendation for multiple viewers based on user profile merging. User Model. User-Adap. Inter. 16(1), 63–82 (2006)CrossRefGoogle Scholar
  12. 12.
    Zimmerman, J., Kauapati, K., Buczak, A., Schaffer, D., Gutta, S., Martino, J.: Tv personalization system. In: Personalized Digital Television. Human-Computer Interaction Series, vol. 6, pp. 27–51. Springer, Netherlands (2004)Google Scholar
  13. 13.
    Lops, P., Gemmis, M., Semeraro, G.: Content-based recommender systems: State of the art and trends. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 73–105. Springer, US (2011)CrossRefGoogle Scholar
  14. 14.
    Bridge, D., Goker, M.H., McGinty, L., Smyth, B.: Case-based recommender systems. Knowl. Eng. Rev. 20(3), 315–320 (2005)Google Scholar
  15. 15.
    Smyth, B.: Case-based recommendation. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web, pp. 342–376. Springer, Berlin (2007)CrossRefGoogle Scholar
  16. 16.
    Bin Ghauth, K., Abdullah, N.: Building an e-learning recommender system using vector space model and good learners average rating. In: Ninth IEEE International Conference on Advanced Learning Technologies (July 2009), pp. 194–196Google Scholar
  17. 17.
    Musto, C.: Enhanced vector space models for content-based recommender systems. In: Proceedings of the Fourth ACM Conference on Recommender Systems. RecSys’ 10, pp. 361–364. ACM, New York, NY, USA (2010)Google Scholar
  18. 18.
    Ekstrand, M.D., Riedl, J.T., Konstan, J.A.: Collaborative filtering recommender systems. Found. Trends Hum. Comput. Interact. 4(2), 81–173 (2010)CrossRefGoogle Scholar
  19. 19.
    Desrosiers, C., Karypis, G.: A comprehensive survey of neighborhood-based recommendation methods. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 107–144. Springer, US (2011)CrossRefGoogle Scholar
  20. 20.
    Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)CrossRefGoogle Scholar
  21. 21.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web. WWW’ 01, pp. 285–295. ACM, New York, NY, USA (2001)Google Scholar
  22. 22.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)CrossRefGoogle Scholar
  23. 23.
    Paterek, A.: Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of KDD Cup Workshop at SIGKDD’07, pp. 39–42. ACM, New York (2007)Google Scholar
  24. 24.
    McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: How accuracy metrics have hurt recommender systems. In: CHI ’06 Extended Abstracts on Human Factors in Computing Systems. CHI EA ’06, pp. 1097–1101. ACM, New York, NY, USA (2006)Google Scholar
  25. 25.
    Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th International Conference on World Wide Web. WWW ’05, pp. 22–32. New York, NY, USA (2005)Google Scholar
  26. 26.
    Sheth, S.K., Bell, J.S., Arora, N., Kaiser, G.E.: Towards diversity in recommendations using social networks. Computer science technical reports, Columbia University (2011)Google Scholar
  27. 27.
    Pu, P., Chen, L., Hu, R.: A user-centric evaluation framework for recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 157–164. ACM, New York, NY, USA (2011)Google Scholar
  28. 28.
    Castells, P., Vargas, S., Wang, J.: Novelty and diversity metrics for recommender systems: Choice, discovery and relevance. In: International Workshop on Diversity in Document Retrieval (DDR 2011) at the 33rd European Conference on Information Retrieval (2011)Google Scholar
  29. 29.
    Celma, O., Herrera, P.: A new approach to evaluating novel recommendations. In: Proceedings of the 2008 ACM Conference on Recommender Systems. RecSys ’08, pp. 179–186. ACM, New York, NY, USA (2008)Google Scholar
  30. 30.
    Weng, L.T., Xu, Y., Li, Y., Nayak, R.: Improving recommendation novelty based on topic taxonomy. In: 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Workshops. (Nov 2007), pp. 115–118Google Scholar
  31. 31.
    Swearingen, K., Sinha, R.: Interaction design for recommender systems. Designing Interact. Syst. 6(12), 312–334 (2002)Google Scholar
  32. 32.
    Hu, R., Pu, P.: Acceptance issues of personality-based recommender systems. In: Proceedings of the Third ACM Conference on Recommender Systems. RecSys’ 09, pp. 221–224. ACM, New York, NY, USA (2009)Google Scholar
  33. 33.
    Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: Proceedings of the Fifth ACM Conference on Digital Libraries. DL ’00, pp. 195–204. ACM, New York, NY, USA (2000)Google Scholar
  34. 34.
    Friedrich, G., Zanker, M.: A taxonomy for generating explanations in recommender systems. AI Mag. 32(3), 90–98 (2011)Google Scholar
  35. 35.
    Bilgic, M., Mooney, R.: Explaining recommendations: Satisfaction vs. promotion. In: Proceedings of Beyond Personalization 2005: A Workshop on the Next Stage of Recommender Systems Research at the 2005 International Conference on Intelligent User Interfaces, San Diego, CA (January 2005)Google Scholar
  36. 36.
    Masthoff, J.: Group modeling: selecting a sequence of television items to suit a group of viewers. User Model. User-Adap. Inter. 14(1), 37–85 (2004)CrossRefGoogle Scholar
  37. 37.
    Jameson, A.: More than the sum of its members: Challenges for group recommender systems. In: Proceedings of the Working Conference on Advanced Visual Interfaces. AVI ’04, pp. 48–54. ACM, New York, NY, USA (2004)Google Scholar
  38. 38.
    Yu, Z., Zhou, X., Hao, Y., Gu, J.: User profile merging based on total distance minimization. In: Proceedings of the 2nd International Conference on Smart homes and health Telematics, IOS Press (2004), pp. 25–32Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Tokyo Institute of TechnologyYokohama, KanagawaJapan

Personalised recommendations