“More Like This” or “Not for Me”: Delivering Personalised Recommendations in Multi-user Environments

  • David Bonnefoy
  • Makram Bouzid
  • Nicolas Lhuillier
  • Kevin Mercer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4511)


The television as a multi-user device presents some specificities with respect to personalisation. Recommendations should be provided both per-viewers as well as for a group. Recognising the inadequacy of traditional user modelling techniques with the constraint of television’s lazy watching usage patterns, this paper presents a new recommendation mechanism based on anonymous user preferences and dynamic filtering of recommendations. Results from an initial user study indicate this mechanism was able to provide content recommendations to individual users within a multi-user environment with a high level of user satisfaction and without the need for user authentication or individual preference profile creation.


Personalisation recommendation preference user model group 


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  1. 1.
    Akaike, H.: A new look at the statistical model identification. IEEE Transactions on Automatic Control 19(6), 716–723 (1974)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Barbieri, M., Ceccarelli, M., Mekenkamp, G., Nesvadba, J.: A Personal TV Receiver with Storage and Retrieval Capabilities. In: Proceedings of workshop on personalization in future TV, 8th Conference on User Modeling (2001)Google Scholar
  3. 3.
    Bonnici, S.: Which Channel Is That On? A Design Model for Electronic Programme Guides. In: Proceedings of the 1st European Conference on Interactive Television: from Viewers to Actors? pp. 49–57 (2003)Google Scholar
  4. 4.
    Cotter, P., Smyth, B.: Personalised Electronic Programme Guides - Enabling Technologies for Digital T. Kunstliche Intelligenz, pp. 37–40 (2001)Google Scholar
  5. 5.
    Das, D., Horst, H.: Recommender Systems for TV. In: Proceedings of 15th AAAI Conference (1998)Google Scholar
  6. 6.
    Gómez-Ballester, E., Micó, L., Oncina, J.: A Fast Approximated k-Median Algorithm. In: Caelli, T.M., Amin, A., Duin, R.P.W., Kamel, M.S., de Ridder, D. (eds.) SPR 2002 and SSPR 2002. LNCS, vol. 2396, pp. 725–733. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Jameson, A.: More than the sum of its members: Challenges for group recommender systems. In: Proceedings of the International Working Conference on Advanced Visual Interfaces, pp. 48–54 (2004)Google Scholar
  8. 8.
    Krumm, J., Shafer, S., Wilson, A.: How a Smart Environment Can Use Perception. In: Workshop on Sensing and Perception for Ubiquitous Computing, part of UbiComp conference (2001)Google Scholar
  9. 9.
    Masthoff, J.: Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers. User Modeling and User-Adapted Interaction 14(1), 37–85 (2004)CrossRefGoogle Scholar
  10. 10.
    O’Connor, M., Cosley, D., Konstan, J.A., Riedl, J.: PolyLens: A Recommender System for Groups of Users. In: Proceedings of the seventh European Conference on Computer Supported Cooperative Work, pp. 199–218 (2001)Google Scholar
  11. 11.
    Pelleg, D., Moore, A.: X-means: Extending K-means with Efficient Estimation of the Number of Clusters. In: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 727–734 (2000)Google Scholar
  12. 12.
    Taylor, A., Harper, R.: Switching on to switch off: An analysis of routine TV watching habits and their implications for electronic program guide design. UsableiTV, pp. 7–13 (2001)Google Scholar
  13. 13.
    Thawani, A., Gopalan, S., Sridhar, V.: Viewing characteristics based personalized ad streaming in an interactive TV environment. In: First IEEE Consumer Communications and Networking Conference, pp. 483–488 (2004)Google Scholar
  14. 14.
    Zhiwen, Y., Xingshe, Z., Yanbin, H., Jianhua, G.: TV Program Recommendation for Multiple Viewers Based on user Profile Merging. Journal User Modeling and User-Adapted Interaction 16(1), 63–82 (2006)CrossRefGoogle Scholar
  15. 15.
    Zuo, F., de With, P.H.N.: Real-time Embedded Face Recognition for Smart Home. IEEE Transactions on Consumer Electronics (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • David Bonnefoy
    • 1
  • Makram Bouzid
    • 1
  • Nicolas Lhuillier
    • 1
  • Kevin Mercer
    • 2
  1. 1.Motorola Labs, Parc des Algorithmes, 91193 Gif-sur-Yvette cedexFrance
  2. 2.Motorola Labs, Jays Close, Viables Industrial Estate, BasingstokeUK

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