Factors influencing privacy concern for explanations of group recommendation

Shabnam Najafian, Amra Delic, Marko Tkalcic, Nava Tintarev

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

3 Citations (Scopus)
2 Downloads (Pure)

Abstract

Explanations can help users to better understand why items have been recommended. Additionally, explanations for group recommender systems need to consider further goals than single-user recommender systems. For example, we need to balance group members' need for privacy with their need for transparency, since a transparent explanation might pose a privacy hazard. In an online experiment with real groups (n=114 participants: 38 groups of size 3), we seek to understand which factors influence people's privacy concerns when a single explanation is presented to a group in the tourism domain. In particular, we study the direct effects of three factors on privacy concern: a) group members' personality (using the ĝ€ Big Five' personality traits), b) specific preference scenarios (i.e., having minority or majority preferences compared to two other group members), c) the type of relationship they have in the group (i.e., loosely coupled heterogeneous, versus tightly coupled homogeneous). We find that for personality two traits, Extroversion, and Agreeableness, each significantly affects the privacy concern. Moreover, having the minority or majority preferences in the group, as well as the type of relationship people have in the group, have a strong and significant influence on participants' privacy concern. These results suggest that explanations presented to groups need to be adapted to all three factors (personality, type of relationship, and preference scenario) when considering the privacy concern of users.

Original languageEnglish
Title of host publicationUMAP 2021 - Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization
PublisherAssociation for Computing Machinery (ACM)
Pages14-23
Number of pages10
ISBN (Electronic)9781450383660
DOIs
Publication statusPublished - 2021
Event29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021 - Virtual, Online, Netherlands
Duration: 21 Jun 202025 Jun 2020

Publication series

NameUMAP 2021 - Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization

Conference

Conference29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021
CountryNetherlands
CityVirtual, Online
Period21/06/2025/06/20

Keywords

  • Explanation
  • Group recommendation
  • Information privacy
  • Privacy concern

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