Forward and Outward: From Aggregate to Individual and Towards Human Understanding in Conversational Recommendation

Abstract

Conversational recommender systems (CRS) have made substantial progress leveraging LLMs for preference elicitation and recommendation. Yet recent evidence suggests the field is approaching a structural ceiling: (1) recommendation accuracy degrades with deeper elicitation through a recall-specificity asymmetry, (2) the field’s most widely used metric shows near-zero correlation with self-reported user satisfaction, and (3) evaluation infrastructure suffers from reliability failures, benchmark biases, and invisibility to deceptive behaviors. This blue sky paper argues that moving past these limits requires a shift along two axes. Forward: from modeling only what users prefer toward understanding how they express preferences and why they hold them, engaging constructs such as linguistic hedging, conversational style, and motivational structure that current systems discard, and shifting from aggregate to individual-level evaluation. Outward: toward transdisciplinary engagement with psycholinguistics and pragmatics, which offer operationalizable tools for interpreting what users implicate beyond what they state, and with behavioral economics and cognitive science, which establish that preferences are constructed through interaction rather than retrieved from stable internal states. We propose the intersection of psycholinguistics and cognitive science as the most productive starting point for modeling users as whole persons rather than collections of attribute preferences.