  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.
