While Conversational Recommender Systems (CRS) have matured technically, they frequently lack principled methods for encoding latent experiential aims as adaptive state variables. Consequently, contemporary architectures often prioritise ranking accuracy at the expense of nuanced, context-sensitive interaction behaviours. This paper addresses this gap through a comprehensive multi-domain study ($N = 168$) that quantifies the joint prioritisation of three critical interaction aims: educative (to inform and justify), explorative (to diversify and inspire), and affective (to align emotionally and socially). Utilising Bayesian hierarchical ordinal regression, we establish domain profiles and perceived item value as systematic modulators of these priorities. Furthermore, we identify stable user-level preferences for autonomy that persist across distinct interactional goals, suggesting that agency is a fundamental requirement of the conversational experience. Drawing on these empirical foundations, we formalise the Recommendation-as-Experience (RAE) adaptation framework. RAE systematically encodes contextual and individual signals into structured state representations, mapping them to experience-aligned dialogue policies realised through retrieval diversification, heuristic logic, or Large Language Model based controllable generation. As an architecture-agnostic blueprint, RAE facilitates the design of context-sensitive CRS that effectively balance experiential quality with predictive performance.

翻译：尽管对话推荐系统（CRS）在技术上已趋于成熟，但其往往缺乏将潜在的体验目标编码为自适应状态变量的系统性方法。因此，当代架构常常以牺牲细致入微、情境敏感的交互行为为代价，优先考虑排序准确性。本文通过一项全面的多领域研究（$N = 168$）来弥补这一差距，该研究量化了对三个关键交互目标的联合优先级排序：教育性（旨在告知与论证）、探索性（旨在多样化与启发）以及情感性（旨在实现情感与社会对齐）。利用贝叶斯层次序数回归，我们确立了领域特征和感知物品价值作为这些优先级的系统性调节因素。此外，我们识别出用户层面对于自主性的稳定偏好，这种偏好贯穿于不同的交互目标，表明自主性是对话体验的一项基本要求。基于这些实证基础，我们形式化了"推荐即体验"（RAE）适应框架。RAE 系统地将情境与个体信号编码为结构化的状态表示，并将其映射到与体验对齐的对话策略，这些策略通过检索多样化、启发式逻辑或基于大语言模型的可控生成来实现。作为一个与架构无关的蓝图，RAE 促进了情境敏感 CRS 的设计，有效平衡了体验质量与预测性能。