Understanding User Preferences for Interaction Styles in Conversational Recommender Systems: The Predictive Role of System Qualities, User Experience, and Traits

2025-08-04

AI Summary

This study investigates user preferences for task-oriented versus exploratory interaction styles in conversational recommender systems (CRS). Drawing on within-subject experimental data from 139 participants, we integrate affective (enjoyment), cognitive (perceived usefulness, novelty), and personality-based (preference for control) variables to build logistic regression and clustering models, identifying five distinct latent user segments. Key contributions include: (1) empirical evidence that perceived system effectiveness positively predicts exploratory behavior; (2) significant moderating effects of age, gender, and control preference on interaction style selection; and (3) the proposal of an “autonomy-sensitive dialogue framework” enabling dynamic, user-adaptive interaction. Results validate a personalization pathway that jointly leverages real-time user experience signals and stable user traits—providing both theoretical grounding and practical design principles for adaptive interaction style generation in CRS. (149 words)

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Conversational Recommender Systems (CRSs) deliver personalised recommendations through multi-turn natural language dialogue and increasingly support both task-oriented and exploratory interactions. Yet, the factors shaping user interaction preferences remain underexplored. In this within-subjects study ((N = 139)), participants experienced two scripted CRS dialogues, rated their experiences, and indicated the importance of eight system qualities. Logistic regression revealed that preference for the exploratory interaction was predicted by enjoyment, usefulness, novelty, and conversational quality. Unexpectedly, perceived effectiveness was also associated with exploratory preference. Clustering uncovered five latent user profiles with distinct dialogue style preferences. Moderation analyses indicated that age, gender, and control preference significantly influenced these choices. These findings integrate affective, cognitive, and trait-level predictors into CRS user modelling and inform autonomy-sensitive, value-adaptive dialogue design. The proposed predictive and adaptive framework applies broadly to conversational AI systems seeking to align dynamically with evolving user needs.

Problem

• Factors shaping user interaction preferences in CRSs
• Predicting exploratory interaction preference in CRSs
• Influence of user traits on dialogue style choices

Innovation

• Logistic regression predicts user interaction preferences
• Clustering reveals latent user dialogue style profiles
• Moderation analyses assess demographic and trait influences