Recommendation-as-Experience: A Framework for Context-Sensitive Adaptation in Conversational Recommender Systems

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Modern conversational recommender systems excel at technical optimisation but often fail to operationalise the latent experiential dimensions that govern human decision-making. Current architectures predominantly prioritise recommendation accuracy, frequently neglecting the nuanced, context-sensitive behaviours required for meaningful interaction. We introduce Recommendation-as-Experience (RAE), a framework designed to bridge this gap by encoding experiential objectives as adaptive state variables. Through a multi-domain study ( N = 168), we employ Bayesian hierarchical ordinal regression to quantify the interplay between three primary interactional pillars: educative (justification and transparency), explorative (discovery and serendipity), and affective (social and emotional resonance). Our findings demonstrate that domain-specific profiles and perceived item value function as systematic modulators of these priorities. Notably, we identify a strong preference for user autonomy within the tested apparel e-commerce context, suggesting that interactional agency is an important component of the conversational experience, though further cross-domain validation is required for higher-stakes scenarios. Drawing on these empirical insights, the RAE framework formalises the mapping of contextual and individual signals onto structured state representations. These representations drive experience-aligned dialogue policies through retrieval diversification, heuristic constraints, or controllable Large Language Model generation. RAE framework provides a principled approach for balancing predictive precision with high-fidelity experiential quality.