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Group Recommender Systems: Combining Individual Models

  • Judith Masthoff
Chapter

Abstract

This chapter shows how a system can recommend to a group of users by aggregating information from individual user models and modelling the users affective state. It summarizes results from previous research in this area. It also shows how group recommendation techniques can be applied when recommending to individuals, in particular for solving the cold-start problem and dealing with multiple criteria.

Keywords

Recommender System Emotional Contagion Aggregation Strategy News Item Ambient Intelligence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Notes

Acknowledgments

Judith Masthoff’s research has been partly supported by Nuffield Foundation Grant No. NAL/00258/G.

References

  1. 1.
    Alfonseca, E., Carro, R.M., Martn, E., Ortigosa, A., Paredes, P.: The Impact of Learning Styles on Student Grouping for Collaborative Learning: A Case Study. UMUAI 16 (2006) 377-401Google Scholar
  2. 2.
    Ardissono, L., Goy,A., Petrone, G., Segnan, M., Torasso, P.: Tailoring the Recommendation of Tourist Information to Heterogeneous User Groups. In S. Reich, M. Tzagarakis, P. De Bra (eds.), Hypermedia: Openness, Structural Awareness, and Adaptivity, InternationalWorkshops OHS-7, SC-3, and AH-3. Lecture Notes in Computer Science 2266, Springer Verlag, Berlin (2002) 280-295Google Scholar
  3. 3.
    Asch,S.E.:Studies of independence and conformity: a minority of one against a unanimous majority. Pschol. Monogr. 70 (1956) 1-70Google Scholar
  4. 4.
    de Campos, L.M., Fernandez-Luna, J.M., Huete, J.F., Rueda-Morales, M.A.: Managing uncertainty in group recommending processes. UMUAI 19 (2009) 207-242Google Scholar
  5. 5.
    Harrer, A., McLaren, B.M., Walker, E., Bollen L., Sewall, J.: Creating Cognitive Tutors for Collaborative Learning: Steps Toward Realization. UMUAI 16 (2006) 175-209Google Scholar
  6. 6.
    Introne, J., Alterman,R.: Using Shared Representations to Improve Coordination and Intent Inference. UMUAI 16 (2006) 249-280Google Scholar
  7. 7.
    Jameson, A.: More than the Sum of its Members: Challenges for Group Recommender Systems. International Working Conference on Advanced Visual Interfaces, Gallipoli, Italy (2004)Google Scholar
  8. 8.
    Jameson, A., Smyth, B.: Recommendation to groups. In: Brusilovsky, P., Kobsa, A., Njedl, W. (Eds). The AdaptiveWeb Methods and Strategies ofWeb Personalization. Springer (2007) 596-627Google Scholar
  9. 9.
    Masthoff, J.: Modeling the multiple people that are me. In: P. Brusilovsky, A.Corbett, and F. de Rosis (eds.) Proceedings of the 2003 User Modeling Conference, Johnstown, PA. Springer Verlag, Berlin (2003) 258-262Google Scholar
  10. 10.
    Masthoff, J.: Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers. UMUAI 14 (2004) 37-85Google Scholar
  11. 11.
    Masthoff, J.: Selecting News to Suit a Group of Criteria: An Exploration. 4th Workshop on Personalization in Future TV - Methods, Technologies, Applications for Personalized TV, Eindhoven, the Netherlands (2004)Google Scholar
  12. 12.
    Masthoff, J., Gatt, A.: In Pursuit of Satisfaction and the Prevention of Embarrassment: Affective state in Group Recommender Systems. UMUAI 16 (2006) 281-319Google Scholar
  13. 13.
    Masthoff, J.: The user as wizard: A method for early involvement in the design and evaluation of adaptive systems. Fifth Workshop on User-Centred Design and Evaluation of Adaptive Systems (2006).Google Scholar
  14. 14.
    Masthoff, J., Vasconcelos,W.W., Aitken, C., Correa da Silva, F.S.: Agent-Based Group Modelling for Ambient Intelligence. AISB symposium on Affective Smart Environments, Newcastle, UK (2007)Google Scholar
  15. 15.
    McCarthy, J., Anagnost, T.: MusicFX: An Arbiter of Group Preferences for Computer Supported Collaborative Workouts. CSCW, Seattle, WA. (1998) 363-372Google Scholar
  16. 16.
    McCarthy, K., McGinty, L., Smyth, B., Salamo, M.: The needs of the many: A casebased group recommender system. European Conference on Case-Based Reasoning, Springer (2006) 196-210 702 Judith MasthoffGoogle Scholar
  17. 17.
    O’ Conner, M., Cosley, D., Konstan, J.A., Riedl, J.: PolyLens: A Recommender System for Groups of Users. ECSCW, Bonn, Germany (2001) 199-218. As accessed on http://www.cs.umn.edu/Research/GroupLens/poly-camera-final.pdf
  18. 18.
    Read, T., Barros, B., Brcena, E., Pancorbo, J.: Coalescing Individual and Collaborative Learning to Model User Linguistic Competences. UMUAI 16 (2006) 349-376Google Scholar
  19. 19.
    Suebnukarn, S., Haddawy, P.: Modeling Individual and Collaborative Problem-Solving in Medical Problem-Based Learning. UMUAI 16 (2006) 211-248Google Scholar
  20. 20.
    Yu, Z., Zhou, X., Hao, Y. Gu, J.: TV Program Recommendation for Multiple Viewers Based on User Profile Merging. UMUAI 16 (2006) 63-82Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.University of AberdeenAberdeenUK

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