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Group Recommender Systems: Aggregation, Satisfaction and Group Attributes

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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 modeling the user’s affective state . It summarizes results from previous research in these areas. It explores how group attributes can be incorporated in aggregation strategies. Additionally, it 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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Notes

  1. 1.

    These strategies are too complicated to fully explain here, see the original papers for details.

  2. 2.

    This does not necessarily mean that these strategies are bad, as complexity can also play a role. In fact, in Experiment 2 Borda count was amongst the best performing strategies.

  3. 3.

    In terms of satisfaction functions predicting the same relative satisfaction scores for group members as predicted by participants, see [28] for details.

  4. 4.

    We transformed a rating r into (r-scale_midpoint)2 if r ≥ scale_midpoint, and -(r-scale_midpoint)2 if r<scale_midpoint.

  5. 5.

    We transformed a rating r by a user u into r × (TotalRatingsAverage ÷ TotalRatings(u)), where TotalRatingsAverage is the sum for all items of the average ratings by all users, and TotalRatings(u) is the sum for all items of u’s rating.

  6. 6.

    In a between-subject design, two different topics were used evoking different moods.

  7. 7.

    For reasons explained in [30], a learning rather than recommender task was used, and satisfaction with performance measured. There was an easy (E), medium (M) and difficult (D) variant of the task, so we could predict accurately how satisfied participants would be with performance on an individual task, and could focus on modeling the effect of sequences on satisfaction. Half the participants did tasks in order E-D-M, the other half in order D-E-M.

  8. 8.

    Additionally, it seems plausible (but requires investigation) that users would be more dissatisfied with disliked selected items when their expertise is higher than that of other group members.

  9. 9.

    This may work well when using an Additive or Multiplicative strategy, but does not really work for the Least Misery and Most Pleasure strategies used in [15], and hence unfortunately some of the formulas in [15] which incorporate expertise lack validity.

  10. 10.

    Personal impact is not completely distinct from Role: somebody’s role may influence their personal impact. However, it is still possible for people with the same official roles to have a different cognitive centrality.

  11. 11.

    We have added the word positional to the terms homogeneous and heterogeneous used in [47] to avoid confusion with the earlier use of these words to indicate how diverse group preferences are.

  12. 12.

    This initially offers the user non-personalized recommendations, however not necessarily by purely using popularity (e.g. Average without Misery can be used and fairness principles can be applied towards the other group members when recommending a sequence).

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Acknowledgement

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

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  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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  1. Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano - Bozen, Italy

    Francesco Ricci

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

    Lior Rokach

  3. Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

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Masthoff, J. (2015). Group Recommender Systems: Aggregation, Satisfaction and Group Attributes. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7637-6_22

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