You Do Not Decide for Me! Evaluating Explainable Group Aggregation Strategies for Tourism

Shabnam Najafian, Daniel Herzog, Sihang Qui, Oana Inel, Nava Tintarev

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
4 Downloads (Pure)

Abstract

Most recommender systems propose items to individual users. However, in domains such as tourism, people often consume items in groups rather than individually. Different individual preferences in such a group can be difficult to resolve, and often compromises need to be made. Social choice strategies can be used to aggregate the preferences of individuals. We evaluated two explainable modified preference aggregation strategies in a between-subject study (n=200), and compared them with two baseline strategies for groups that are also explainable, in two scenarios: high divergence (group members with different travel preferences) and low divergence (group members with similar travel preferences). Generally, all investigated aggregation strategies performed well in terms of perceived individual and group satisfaction and perceived fairness. The results also indicate that participants were sensitive to a dictator-based strategy, which affected both their individual and group satisfaction negatively (compared to the other strategies).
Original languageEnglish
Title of host publicationProceedings of the 31st ACM Conference on Hypertext and Social Media (HT ’20),
Pages 187–196
Number of pages10
ISBN (Electronic)978-1-4503-7098-1
DOIs
Publication statusPublished - 2020
EventHT'20: 31st ACM Conference on Hypertext and Social Media - Online event, United States
Duration: 13 Jul 202015 Jul 2020
Conference number: 31

Conference

ConferenceHT'20: 31st ACM Conference on Hypertext and Social Media
Abbreviated titleHT'20
Country/TerritoryUnited States
CityOnline event
Period13/07/2015/07/20
OtherVirtual/online event due to COVID-19

Keywords

  • Explainable aggregation strategies
  • Group recommendation
  • Human-centered computing user studies

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