Explanation Driving Exploration: Aligning Conversational Recommender Systems with Users' Exploratory Information Needs

LLM-powered conversational recommender systems (CRSs) empower users to personalize recommendation services, giving them control over how recommendations are represented and explained. Explanations of why particular options are recommended are shown to be especially valuable when users explore unfamiliar items. While prior work on personalized explanations in recommender systems has focused predominantly on explanation style, there is still little understanding of what types of information explanations should contain to meaningfully support users’ exploration. To allow CRSs to better align the explanations with users’ informational needs, in this paper, we present the information composition for recommendation explanations. Informed by an exploratory interview-based user study, we propose four key informational dimensions: Essence, Experience, Exchange, and Entwinement. We then report a comparative evaluation showing that explanations structured along these dimensions are perceived as more supportive of engagement-related outcomes than baseline LLM-generated explanations. We conclude by outlining design implications for LLM-powered CRSs.