Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems

TL;DR

This study explores how personal characteristics like personality, trust propensity, and domain knowledge affect user trust in conversational recommender systems, highlighting the importance of personalized approaches for building trust.

Contribution

It provides empirical evidence on the influence of personal traits on trust in CRSs and examines how different system interaction styles interact with these traits.

Findings

Trust propensity and domain knowledge positively influence trust.

High conscientiousness correlates with trust in mixed-initiative CRSs.

User characteristics significantly impact trust levels.

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.