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Group Recommender Systems: Combining Individual Models

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Recommender Systems Handbook

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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.

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Acknowledgments

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

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Authors and Affiliations

  1. University of Aberdeen, AB24 3UE, Aberdeen, UK

    Judith Masthoff

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  1. Judith Masthoff
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Correspondence to Judith Masthoff .

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Editors and Affiliations

  1. , Faculty of Computer Science, Free University of Bozen-Bolzano, Piazza Domenicani 3, Bolzano, 39100, Italy

    Francesco Ricci

  2. , Dept. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel

    Lior Rokach

  3. Dept. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

  4. School of Communication,, Information & Library Studies, Rutgers University, Huntington Street 4, New Brunswick, 08901-1071, New Jersey, USA

    Paul B. Kantor

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Masthoff, J. (2011). Group Recommender Systems: Combining Individual Models. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-85820-3_21

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  • DOI: https://doi.org/10.1007/978-0-387-85820-3_21

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  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-85819-7

  • Online ISBN: 978-0-387-85820-3

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