To Share or Not to Share: Understanding and Modeling Individual Disclosure Preferences in Recommender Systems for the Workplace

Geoff Musick, Wen Duan, Shabnam Najafian, Subhasree Sengupta, Christopher Flathmann, Bart Knijnenburg, Nathan McNeese

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Newly-formed teams often encounter the challenge of members coming together to collaborate on a project without prior knowledge of each other’s working and communication styles. This lack of familiarity can lead to conflicts and misunderstandings, hindering effective teamwork. Derived from research in social recommender systems, team recommender systems have shown the ability to address this challenge by providing personality-derived recommendations that help individuals interact with teammates with differing personalities. However, such an approach raises privacy concerns as to whether teammates would be willing to disclose such personal information with their team. Using a vignette survey conducted via a research platform that hosts a team recommender system, this study found that context and individual differences significantly impact disclosure preferences related to team recommender systems. Specifically, when working in interdependent teams where success required collective performance, participants were more likely to disclose personality information related to Emotionality and Extraversion unconditionally. Drawing on these findings, this study created and evaluated a machine learning model to predict disclosure preferences based on group context and individual differences, which can help tailor privacy considerations in team recommender systems prior to interaction.

Original languageEnglish
Article number9
Number of pages28
JournalProceedings of the ACM on Human-Computer Interaction
Volume8
Issue number1
DOIs
Publication statusPublished - 2024

Keywords

  • Additional Key Words and Phrases: Group recommender systems
  • Individual difference
  • Privacy
  • Teamwork

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