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Conversational recommender systems (CRSs) imitate human advisors to assistusers in finding items through conversations and have recently gainedincreasing attention in domains such as media and e-commerce. Like in humancommunication, building trust in human-agent communication is essential givenits significant influence on user behavior. However, inspiring user trust inCRSs with a "one-size-fits-all" design is difficult, as individual users mayhave their own expectations for conversational interactions (e.g., who, user orsystem, takes the initiative), which are potentially related to their personalcharacteristics. In this study, we investigated the impacts of three personalcharacteristics, namely personality traits, trust propensity, and domainknowledge, on user trust in two types of text-based CRSs, i.e., user-initiativeand mixed-initiative. Our between-subjects user study (N=148) revealed thatusers' trust propensity and domain knowledge positively influenced their trustin CRSs, and that users with high conscientiousness tended to trust themixed-initiative system.

Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems