理解对话式推荐系统中用户对交互风格的偏好：系统质量、用户体验和特质的预测作用
Understanding User Preferences for Interaction Styles in Conversational Recommender Systems: The Predictive Role of System Qualities, User Experience, and Traits
AI总结：
本研究通过受试者内实验发现，愉悦感、有用性、新颖性和对话质量等预测用户对探索性交互的偏好，并识别出五种用户画像，为对话式推荐系统的自适应设计提供依据。
摘要：
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，并日益支持面向任务和探索性的交互。然而，影响用户交互偏好的因素仍未得到充分探索。在这项受试者内研究中（N = 139），参与者体验了两种脚本化的CRS对话，评价了他们的体验，并指出了八种系统质量的重要性。逻辑回归显示，对探索性交互的偏好由愉悦感、有用性、新颖性和对话质量预测。出乎意料的是，感知有效性也与探索性偏好相关。聚类分析揭示了五种具有不同对话风格偏好的潜在用户画像。调节分析表明，年龄、性别和控制偏好显著影响这些选择。这些发现将情感、认知和特质层面的预测因素整合到CRS用户建模中，并为自主性敏感、价值自适应的对话设计提供信息。所提出的预测和自适应框架广泛适用于寻求与不断变化的用户需求动态对齐的对话式AI系统。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
