English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Pro

Presents the zaia usage as premium feature

ZAIA

Presents AI Discovery search usage as a premium feature

AI Discovery

Presents AI Discovery explain and summary usage as a premium feature

Content Analysis

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Presents the pdf analysis as premium feature

PDF Analysis

1500 Top-up

3000 Top-up

Zendy Basic

2500 Top-up

Zendy Unlimited

Presents the access of premium content as premium feature

Premium Content

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