Recommendation-as-Experience: A framework for context-sensitive adaptation in conversational recommender systems

TL;DR

This paper introduces the Recommendation-as-Experience (RAE) framework, which enhances conversational recommender systems by encoding experiential aims and user preferences for more nuanced, context-sensitive interactions.

Contribution

It presents a novel, empirically grounded framework that systematically encodes contextual and individual signals to improve interaction quality in CRS.

Findings

Identified stable user preferences for autonomy across interaction goals.

Quantified the importance of educative, explorative, and affective aims in CRS.

Established domain profiles and perceived item value as modulators of interaction priorities.

Abstract

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.