研究用户在对话推荐系统中的互动偏好，发现体验质量与个人特质影响选择。
Understanding User Preferences for Interaction Styles in Conversational Recommender Systems: The Predictive Role of System Qualities, User Experience, and Traits
- 通过多轮对话实验，分析系统品质、用户体验与用户特质对互动风格的影响。
- 54%用户偏好探索型交互，其关键预测因素为趣味性、实用性与对话质量。
- 发现五类用户画像，年龄、性别与控制欲显著调节互动偏好选择。
对话式推荐系统（CRS）通过多轮自然语言对话提供个性化推荐，支持任务导向与探索性交互。本研究采用被试内设计（N=139），参与者体验两段预设对话，评估体验并判断八项系统品质的重要性。逻辑回归显示，探索性互动偏好由趣味性、实用性、新颖性及对话质量共同预测。意外发现感知有效性也与探索偏好正相关。聚类分析揭示五类潜在用户群体，具有不同的对话风格偏好。调节分析表明，年龄、性别与控制偏好显著影响选择。研究将情感、认知与个体特质纳入用户建模，为自主性敏感、价值自适应的对话设计提供依据。所提预测与自适应框架可广泛应用于需动态匹配用户需求的对话AI系统。
原文摘要 · Abstract (English)
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.
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