  Using chatbots to deliver recommendations is increasingly popular. The design
of recommendation chatbots has primarily been taking an information-centric
approach by focusing on the recommended content per se. Limited attention is on
how social connection and relational strategies, such as self-disclosure from a
chatbot, may influence users' perception and acceptance of the recommendation.
In this work, we designed, implemented, and evaluated a social chatbot capable
of performing three different levels of self-disclosure: factual information
(low), cognitive opinions (medium), and emotions (high). In the evaluation, we
recruited 372 participants to converse with the chatbot on two topics: movies
and COVID-19 experiences. In each topic, the chatbot performed small talks and
made recommendations relevant to the topic. Participants were randomly assigned
to four experimental conditions where the chatbot used factual, cognitive,
emotional, and adaptive strategies to perform self-disclosures. By training a
text classifier to identify users' level of self-disclosure in real-time, the
adaptive chatbot can dynamically match its self-disclosure to the level of
disclosure exhibited by the users. Our results show that users reciprocate with
higher-level self-disclosure when a recommendation chatbot consistently
displays emotions throughout the conversation. Chatbot's emotional disclosure
also led to increased interactional enjoyment and more positive interpersonal
perception towards the bot, fostering a stronger human-chatbot relationship and
thus leading to increased recommendation effectiveness, including a higher
tendency to accept the recommendation. We discuss the understandings obtained
and implications to future design.
