研究对话推荐系统中不同对话风格对用户偏好获取的影响
Should We Tailor the Talk? Understanding the Impact of Conversational Styles on Preference Elicitation in Conversational Recommender Systems
- 对比高投入与高体贴两种对话风格，分析其对交互效果的影响
- 允许用户在风格间切换能显著提升满意度和推荐效果
- 研究结果对设计更人性化对话推荐系统有重要参考价值
对话式推荐系统（CRS）通过互动帮助用户表达偏好并获得实时个性化推荐。系统成功与否高度依赖于偏好获取过程。现有研究多关注提问内容，却忽视了语气、节奏和主动性等更广泛的交互模式对任务完成度的影响。本研究在学术文献推荐场景下，对比了两种不同对话风格：高投入型（快节奏、直接、主动，频繁提示）与高体贴型（礼貌、包容，强调清晰与用户舒适），并设置可自由切换风格的灵活条件。结果表明，根据用户专业程度调整对话策略，并提供风格切换灵活性，可显著提升用户满意度与推荐有效性。研究为未来对话推荐系统的交互设计提供了重要启示。
原文摘要 · Abstract (English)
Conversational recommender systems (CRSs) provide users with an interactive means to express preferences and receive real-time personalized recommendations. The success of these systems is heavily influenced by the preference elicitation process. While existing research mainly focuses on what questions to ask during preference elicitation, there is a notable gap in understanding what role broader interaction patterns including tone, pacing, and level of proactiveness play in supporting users in completing a given task. This study investigates the impact of different conversational styles on preference elicitation, task performance, and user satisfaction with CRSs. We conducted a controlled experiment in the context of scientific literature recommendation, contrasting two distinct conversational styles, high involvement (fast paced, direct, and proactive with frequent prompts) and high considerateness (polite and accommodating, prioritizing clarity and user comfort) alongside a flexible experimental condition where users could switch between the two. Our results indicate that adapting conversational strategies based on user expertise and allowing flexibility between styles can enhance both user satisfaction and the effectiveness of recommendations in CRSs. Overall, our findings hold important implications for the design of future CRSs.
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