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Individual and Group Decision Making and Recommender Systems

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Abstract

Given that an important function of recommender systems is to help people make better choices, people who design and study recommender systems ought to have a good understanding of how people make choices and how human choice can be supported. This chapter uses an accessible summary of what is known about these topics as a framework for discussing the implications of this knowledge for the design of recommender systems. The first half of the chapter focuses on choices made by individuals, providing a compact update of the corresponding chapter in the previous edition of this handbook. The second half of the chapter extends the analysis to choices made by groups and their support by recommender systems for groups. Each half is organized in terms of two previously published models that make the relevant knowledge from psychology and related fields accessible to those who work on recommender systems and other interactive computing technology. The Aspect model distinguishes six choice patterns that together capture the wide variety of ways in which people make choices; the model enables us to identify both familiar and novel ways in which recommender systems can support choice. The Arcades model distinguishes seven high-level choice support strategies; whereas one of the strategies is already widely used in recommender systems, the other strategies can help round out the choice support that a recommender system offers.

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Notes

  1. 1.

    There is no crisp distinction in English between “choices” and “decisions”, though the latter term tends to be used more in connection with relatively deliberate choice processes. This chapter uses both terms, depending on which one fits better in a given context.

  2. 2.

    In this chapter, generically used personal pronouns alternate systematically between the masculine and feminine forms on an example-by-example basis.

  3. 3.

    A discussion of the relationship between techniques for choice support and for persuasion is offered on a general level by Jameson et al. [53, sect. 1.2.2] and in the context of recommender systems by Starke et al. [99].

  4. 4.

    Much more detail on these models will be found in the book by Jameson et al. [53], which is freely available via https://chusable.com/foundations.html.

  5. 5.

    Aspect is an acronym formed from the first letters of the six patterns.

  6. 6.

    A detailed introduction to the first six strategies is given by Jameson et al. [53, chap 4]; the seventh strategy is being introduced in this chapter for the first time.

  7. 7.

    Applying the strategy Evaluate on Behalf of the Chooser does not always require recommendation technology; for example, a recommendation like “You are advised to close all other applications before proceeding with the installation procedure” is simply formulated once by the designer of the installation procedure.

  8. 8.

    This type of advice is often given implicitly in the sense that the system provides support for one procedure but not for others.

  9. 9.

    With some decisions made by groups, there exist stakeholders whose interests need to be taken into account even though they are not among the group members who are participating directly in the group decision making process (see, e.g., [46, chap. 1]). For example, the parents of a family may make decisions about a family outing without fully including the children in the decision making process. Doing justice to the interests of absent stakeholders raises issues that cannot be addressed within this chapter (cf. the discussion of “virtual group members” in Chapter “Group Recommender Systems: Beyond Preference Aggregation”).

  10. 10.

    Approach 2 can in principle be applied in Scenarios 3 and 4 in support of Approach 1: A group recommender system might be able to support interaction more effectively if it is able to predict what the results of the interaction would be without its support.

  11. 11.

    Some computational methods have been presented with reference to example computations but to our knowledge not yet deployed in recommender systems (see, e.g., [78]).

  12. 12.

    With regard to a choice made by an individual within a group, the contributions of the other group members can largely be viewed as part of the context for the choice; hence ideas from the area of context-aware recommendation (Chapter “Context-Aware Recommender Systems: From Foundations to Recent Developments”) have some relevance.

  13. 13.

    Even this simple form of group choice can be made more complex by differences among the interests of group members: If the previously chosen solution was more desirable for some group members than for others, the group may choose a different solution on the current occasion in order to balance the group members’ satisfaction over time.

  14. 14.

    This concept differs from people-to-people reciprocal recommendation (Chapter “People-to-People Reciprocal Recommenders”), in which two or more persons are recommended to each other.

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Authors and Affiliations

  1. Chusable AG, Gamprin, Liechtenstein

    Anthony Jameson

  2. Eindhoven University of Technology and JADS, Eindhoven, The Netherlands

    Martijn C. Willemsen

  3. Graz University of Technology, Graz, Austria

    Alexander Felfernig

Authors
  1. Anthony Jameson
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  2. Martijn C. Willemsen
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  3. Alexander Felfernig
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Corresponding author

Correspondence to Anthony Jameson .

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Editors and Affiliations

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

    Francesco Ricci

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

    Lior Rokach

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

    Bracha Shapira

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Jameson, A., Willemsen, M.C., Felfernig, A. (2022). Individual and Group Decision Making and Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, New York, NY. https://doi.org/10.1007/978-1-0716-2197-4_21

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  • DOI: https://doi.org/10.1007/978-1-0716-2197-4_21

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  • Publisher Name: Springer, New York, NY

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  • Online ISBN: 978-1-0716-2197-4

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