论文标题
个人特征对用户信任对会话推荐系统的影响
Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems
论文作者
论文摘要
会话推荐系统（CRSS）模仿人类顾问，以帮助用户通过对话来查找项目，并最近在媒体和电子商务等领域中引起了人们的关注。像在人类交流中一样，鉴于其对用户行为的重大影响，对人类代理交流的建立信任至关重要。但是，很难激发用户对CRS的信任，因为单个用户可能对对话互动有自己的期望（例如，用户或系统采取主动权）可能与他们的个人特征有关。在这项研究中，我们调查了三种个人特征，即个性特征，信任倾向和领域知识的影响，对两种基于文本的CRS的用户信任，即用户发射和混合定位。我们的受试者间用户研究（n = 148）表明，用户的信任倾向和领域知识对他们对CRS的信任产生了积极影响，并且具有较高认真性的用户倾向于信任混合启发性系统。
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.
