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Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems

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Li ChenAuthors Info & Claims

CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

Article No.: 489, Pages 1 - 14

https://doi.org/10.1145/3491102.3517471

Published: 29 April 2022 Publication History

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Abstract

Conversational recommender systems (CRSs) imitate human advisors to assist users in finding items through conversations and have recently gained increasing attention in domains such as media and e-commerce. Like in human communication, building trust in human-agent communication is essential given its significant influence on user behavior. However, inspiring user trust in CRSs with a “one-size-fits-all” design is difficult, as individual users may have their own expectations for conversational interactions (e.g., who, user or system, takes the initiative), which are potentially related to their personal characteristics. In this study, we investigated the impacts of three personal characteristics, namely personality traits, trust propensity, and domain knowledge, on user trust in two types of text-based CRSs, i.e., user-initiative and mixed-initiative. Our between-subjects user study (N=148) revealed that users’ trust propensity and domain knowledge positively influenced their trust in CRSs, and that users with high conscientiousness tended to trust the mixed-initiative system.

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Zhang XChao CChiu YChen H(2024)Study of the Correlation between Streaming Video Platform Content on Food Production Processes and the Behavioral Intentions of Generation ZFoods10.3390/foods1310153713:10(1537)Online publication date: 15-May-2024
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Bauer CBagchi CHundogan Ovan Es K(2024)Where Are the Values? A Systematic Literature Review on News Recommender SystemsACM Transactions on Recommender Systems10.1145/36548052:3(1-40)Online publication date: 5-Jun-2024
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Ma YZiegler JBoratto LGena CMarras MGermanakos PPopescus E(2024)The Effect of Proactive Cues on the Use of Decision Aids in Conversational Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665186(305-315)Online publication date: 27-Jun-2024
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Index Terms

Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI
    2. HCI design and evaluation methods
      1. User models
      2. User studies
  2. Interaction design
    1. Empirical studies in interaction design
    2. Interaction design process and methods
      1. User interface design
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

April 2022

10459 pages

ISBN:9781450391573

DOI:10.1145/3491102

Editors:
Simone Barbosa
PUC-Rio, Brazil
,
Cliff Lampe
University of Michigan, USA
,
Caroline Appert
Université Paris-Saclay, France
,
David A. Shamma
Toyota Research Institute, USA
,
Steven Drucker
Microsoft Research, USA
,
Julie Williamson
University of Glasgow, UK
,
Koji Yatani
University of Tokyo, Japan

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Published: 29 April 2022

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