=Paper=
{{Paper
|id=Vol-2948/invited
|storemode=property
|title=Group Decision Making and Group Recommender Systems
|pdfUrl=https://ceur-ws.org/Vol-2948/invited.pdf
|volume=Vol-2948
|authors=Anthony Jameson
|dblpUrl=https://dblp.org/rec/conf/recsys/Jameson21
}}
==Group Decision Making and Group Recommender Systems==
<pdf width="1500px">https://ceur-ws.org/Vol-2948/invited.pdf</pdf>
<pre>
Group Decision Making and Group Recommender Systems
Anthony Jameson 1 1
1
    Chusable AG, Oberbühl 45, Gamprin, Liechtenstein




1. Abstract
    Like designers of recommender systems for individuals, those who design recommender systems
for groups can benefit greatly from a thorough understanding of human decision making and ways of
supporting it ‒ in particular decision making that occurs in a group context. But a comprehensive
analysis with regard to groups has so far been lacking in the recommender systems field. This talk will
present such an analysis in an accessible way, referring workshop participants to a recently completed
chapter for the Recommender Systems Handbook (3rd edition) for further details and references [1] (a
preprint is available on request from the first author).
    Relevant knowledge about decision making in groups can be found in research on group decision
making, group dynamics more generally, negotiation, and group decision support systems as well as in
the practical experience of human group facilitators. To see the relevance of these areas, we will look
at seven widely accepted principles derived from them that have important implications for the design
of group recommender systems.
    We will then consider two high-level approaches to using this type of knowledge in a recommender
systems context: supporting interaction among group members vs. predicting the results of interaction
without allowing the interaction to occur. The approaches differ greatly in their applicability to
particular recommendation scenarios.
    We will discuss concrete examples of how group recommender systems have applied ‒ or could
apply ‒ these ideas and results. These examples are organized in terms of an extension to groups of the
previously published Aspect and Arcades models, which distinguish the diverse ways in which people
make choices and in which their choice processes can be supported.

2. Bio
    Anthony Jameson's contributions to the field of recommender systems have focused on showing
how an understanding of the psychological processes involved in individual and group decision making
helps to generate new ideas for the design of recommender systems.
    His chapter with Barry Smith titled Recommendation to Groups (2007) is one of the most influential
publications on its topic. More general contributions include the book Choice Architecture for Human-
Computer Interaction and the cofounding of the ACM Transactions on Interactive Intelligent Systems.
    In 2017, he left his position as Principal Researcher at DFKI to found the startup Chusable AG
(https://chusable.com), which creates software for the support of everyday decision making and the
sharing of actionable knowledge.




IntRS’21: Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, September 25, 2021,
Virtual Event
EMAIL: anthonyjameson@chusable.com
             ©️ 2021 Copyright for this paper by its authors.
             Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
             CEUR Workshop Proceedings (CEUR-WS.org)
3. References
[1] A. Jameson, M. Willemsen, and A. Felfernig, Individual and group decision making and
    recommender systems, in: F. Ricci, L. Rokach, and B. Shapira (Ed.), Recommender systems
    handbook, 3rd. ed., Springer, Berlin, 2022.

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