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

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.