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TV Personalization System

Design of a TV Show Recommender Engine and Interface
  • John Zimmerman
  • Kaushal Kauapati
  • Anna L. Buczak
  • Dave Schaffer
  • Srinivas Gutta
  • Jacquelyn Martino
Part of the Human-Computer Interaction Series book series (HCIS, volume 6)

Abstract

The arrival of PVRs (Personal Video Recorders)—tape less devices that allow for easy navigation and storage of TV content—and the availability of hundreds of TV channels in US homes have made the task of finding something good to watch increasingly difficult. In order to ease this content selection overload problem, we pursued three related research themes. First, we developed a recommender engine that tracks users’ TV-preferences and delivers accurate content recommendations. Second, we designed a user interface that allows easy navigation of selections and easily affords inputs required by the recommender engine. Third, we explored the importance of gaining users’ trust in the recommender by automatically generating explanations for content recommendations. In evaluation with users, our smart interface came out on top beating TiVo’s interface and TV Guide Magazine, in terms of usability, fun, and quick access to TV shows of interest. Further, our approach of combining multiple recommender ratings—resulting from various machine-learning methods—using neural networks has produced very accurate content recommendations.

Key words

electronic program guide (EPG) interactive TV, personalization, trust TV interface TV recommender user interface 

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References

  1. Ardissono, L., Gena, C., Torasso, P., Bellifemine, F., Chiarotto, A., Difino, A. and Negro, B.: User Modeling and Recommendation Techniques for Personalized Electronic Program Guides. This Volume.Google Scholar
  2. Smyth, B. and Cotter, P.: The Evolution of the Personalized Electronic Program Guide. This Volume.Google Scholar
  3. Balabanovic, M. and Shoham, Y.: 1997, FAB: Content-Based Collaborative Recommender. Communications of the ACM 40(3), 66–72.CrossRefGoogle Scholar
  4. Barneveld, J. V. and Setten, M. V.: Designing Usable Interfaces for TV Recommender. This Volume.Google Scholar
  5. Baudisch, P. and Bruekner, L.: 2001, TV Scout: Guiding Users from Printed TV Program Guides to Personalized TV Recommendation. Second International Conference on Adaptive Hypermedia and Adaptive Web Based Systems: Workshop on Personalization in Future TV, Malaga, Spain, pp. 151–160.Google Scholar
  6. Bickmore, T. and Cassell, J.: 2001, Relational Agents: A Model and Implementation of Building User Trust. Conference on Human Factors in Computing, Seattle, WA, USA, pp. 80–87.Google Scholar
  7. Billsus, D. and Pazzani, M. J.: 1998, Learning Collaborative Information Filters. Fifteenth International Conference on Machine Learning, Wisconsin, USA, pp. 46–54.Google Scholar
  8. Cotter, P. and Smyth, B.: 2000, PTV: Intelligent Personalized TV Guides. Seventeenth National Conference on Artificial Intelligence, Austin, TX, USA, pp. 957–964.Google Scholar
  9. Das, D. and ter Horst, H.: 1998, Recommender Systems for TV. Technical Report WS-98-08 Recommender System, Papers from the 1998 Workshop, Madison, WI. Menlo Park, CA: AAAI Press, pp. 35–36.Google Scholar
  10. Duda, R. and Hart, P.: 1973, Pattern Recognition and Scene Analysis. John Wiley & Sons, New York.Google Scholar
  11. Fogg, B. J. and Tseng, H.: 1999, The Elements of Computer Credibility. Conference on Human Factors in Computing Systems, Pittsburgh, PA, USA, pp. 80–86.Google Scholar
  12. Gutta, S., Kurapati, K., Lee, K. P., Martino, J., Milanski, J., Schaffer, D. and Zimmerman, J.: 2000, TV Content Recommender System. Seventeenth National Conference on Artificial Intelligence, Austin, TX, USA, pp. 1121–1122.Google Scholar
  13. Hammond, K. J., Burke, R. and Schmitt, K.: 1996, A Case-Based Approach to Knowledge Navigation. In D. Leake (ed.), Case-Based Reasoning Experiences, Lessons and Future Directions, Cambridge, MA: MIT Press, pp. 125–136.Google Scholar
  14. Herlocker, J., Konstan, J. and Riedl, J.: 2000, Explaining Collaborative Filtering Recommendations. Conference on Computer Supported Cooperative Work, Philadelphia, PA, USA, pp. 241–250.Google Scholar
  15. Kubey, R. and Csikszentmihalyi, M.: 1990, Television and the Quality of Life: How Viewing Shapes Everyday Experiences. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  16. Kurapati, K., Gutta, S., Schaffer, D., Martino, J. and Zimmerman, J.: 2001, A Multi-Agent TV Recommender. Eighth International Conference on User Modeling: Workshop on Personalization in Future TV, Sonthofen, Germany, http://www.di.unito.it/~liliana/UM01/kurapati.pdf.
  17. Lee, H., Nam, J., Bae, B., Kim, M., Kang, K. and Kim, J.: 2001, Personalized Contents Guide and Browsing based on User Preference. Second International Conference on Adaptive Hypermedia and Adaptive Web Based Systems: Workshop on Personalization in Future TV, Malaga, Spain, pp. 131–150.Google Scholar
  18. Lerch, F. J. and Prietula, M. J.: 1989, How do we Trust Machine Advice? In: G. Salvendy and M. J. Smith (eds.), Designing and Using Human-Computer Interfaces and Knowledge Based Systems, Amsterdam, the Netherlands: Elsevier Science Publishers B. V., pp. 410–419.Google Scholar
  19. Lull, J.: 1990, Inside Family Viewing: Ethnographic Research on Television’s Audiences. New York: Routledge.Google Scholar
  20. Moody, J. and Darken, C. J.: 1989, Fast Learning in Networks of Locally Tuned Processing Units. Neural Computation 1(2), pp. 281–294.Google Scholar
  21. Mueleman, P., Heister, A., Kohar, H. and Tedd, D.: 1998, Double Agents — Presentation and Filtering Agents for a Digital Television Recording System. Conference on Human Factors in Computing Systems, Los Angeles, CA, USA, pp. 3–4.Google Scholar
  22. Pittarello, F.: Time-pillars World: A 3D Paradigm for the New Enlarged TV Information Domain. This Volume.Google Scholar
  23. Predictive Media, Inc. — http://www.predictivemedia.com/
  24. Quinlan, J. R.: 1983, Learning Efficient Classification Procedures and their Application to Chess End Games. In: R. S. Michalski, J. G. Carbonell and T. M. Mitchell (eds.), Machine Learning: An Artificial Approach, Vol. 1. Morgan Kaufmann Publishers Inc, Palo Alto, California.Google Scholar
  25. Quinlan, J. R.: 1991, C4.5: Machine Learning Programs, Morgan Kaufmann Publishers, Palo Alto, California.Google Scholar
  26. Smyth, B. and Cotter, P.: 1999, Surfing the Digital Wave: Generating Personalised TV Listings using Collaborative Case-Based Recommendations. International Conference on Case-Based Reasoning, Springer-Verlag, Germany, pp. 561–571.Google Scholar
  27. Smyth, B. and Cotter, P.: The Evolution of the Personalized Electronic Program Guide. This Volume.Google Scholar
  28. TiVo, Inc. — http://www.TiVo.com
  29. Tribune Media Services — http://www.tms.tribune.com/
  30. Wheeless, L. and Grotz, J.: 1977, The Measurement of Trust and Its Relationship to Selfdisclosure. Communication Research 3(3), pp. 250–257.Google Scholar
  31. Zimmerman, J. and Kurapati, K.: 2002, Exposing Profiles to Build Trust in a Recommender. Conference on Human Factors in Computing Systems, Minneapolis, MN, pp. 608–609.Google Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • John Zimmerman
    • 1
  • Kaushal Kauapati
  • Anna L. Buczak
  • Dave Schaffer
  • Srinivas Gutta
  • Jacquelyn Martino
  1. 1.Carnegie Mellon UniversityUSA

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