Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems

Wanling Cai; Yucheng Jin; Li Chen

doi:10.1145/3491102.3517471

Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems

Wanling Cai, Yucheng Jin, Li Chen

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

25 Citations (Scopus)

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.

Original language	English
Title of host publication	CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Publisher	Association for Computing Machinery (ACM)
Number of pages	14
ISBN (Print)	9781450391573
DOIs	https://doi.org/10.1145/3491102.3517471
Publication status	Published - Apr 2022
Event	2022 CHI Conference on Human Factors in Computing Systems, CHI 2022 - New Orleans, LA, United States Duration: 30 Apr 2022 → 5 May 2022 https://chi2022.acm.org/ https://dl.acm.org/doi/proceedings/10.1145/3491102

Publication series

Name	Proceedings of the CHI Conference on Human Factors in Computing Systems

Conference

Conference	2022 CHI Conference on Human Factors in Computing Systems, CHI 2022
Country/Territory	United States
City	New Orleans, LA
Period	30/04/22 → 5/05/22
Internet address	https://chi2022.acm.org/ https://dl.acm.org/doi/proceedings/10.1145/3491102

Scopus Subject Areas

Software
Human-Computer Interaction
Computer Graphics and Computer-Aided Design

User-Defined Keywords

Conversational recommender systems
mixed-initiative interaction
personal characteristics
trust

Access to Document

10.1145/3491102.3517471

Cite this

Cai, W., Jin, Y., & Chen, L. (2022). Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems. In CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems Article 489 (Proceedings of the CHI Conference on Human Factors in Computing Systems). Association for Computing Machinery (ACM). https://doi.org/10.1145/3491102.3517471

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title = "Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems",

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.",

keywords = "Conversational recommender systems, mixed-initiative interaction, personal characteristics, trust",

author = "Wanling Cai and Yucheng Jin and Li Chen",

note = "Funding Information: The work was supported by Hong Kong Research Grants Council (RGC/HKBU12201620) and Hong Kong Baptist University IRCMS Project (IRCMS/19-20/D05). We also thank all participants for their time in taking part in our experiment and reviewers for their constructive comments on our paper. Publisher Copyright: {\textcopyright} 2022 ACM.; 2022 CHI Conference on Human Factors in Computing Systems, CHI 2022 ; Conference date: 30-04-2022 Through 05-05-2022",

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doi = "10.1145/3491102.3517471",

language = "English",

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publisher = "Association for Computing Machinery (ACM)",

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Cai, W, Jin, Y & Chen, L 2022, Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems. in CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems., 489, Proceedings of the CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery (ACM), 2022 CHI Conference on Human Factors in Computing Systems, CHI 2022, New Orleans, LA, United States, 30/04/22. https://doi.org/10.1145/3491102.3517471

Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems. / Cai, Wanling; Jin, Yucheng; Chen, Li.
CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery (ACM), 2022. 489 (Proceedings of the CHI Conference on Human Factors in Computing Systems).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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N1 - Funding Information: The work was supported by Hong Kong Research Grants Council (RGC/HKBU12201620) and Hong Kong Baptist University IRCMS Project (IRCMS/19-20/D05). We also thank all participants for their time in taking part in our experiment and reviewers for their constructive comments on our paper. Publisher Copyright: © 2022 ACM.

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N2 - 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.

AB - 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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Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems

Abstract

Publication series

Conference

Scopus Subject Areas

User-Defined Keywords

Access to Document

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