让对话式购物助手更懂用户需求，少问多准。
Dialogue to Discovery: Attribute-Aware Preference Elicitation for Conversational Product Search Assistants
- 根据商品属性动态选问题，优先问最有用的
- 找对目标商品准确率提升超22%，对话缩短近三成
- 适合做智能客服或电商导购系统的研发者
对话式产品搜索助手提供了比传统关键词搜索更自然、更具表现力的交互方式。受限于屏幕空间，仅展示少数商品，因此需要精准获取用户偏好，但过度追问易引发用户疲劳和会话中断；反之，未充分理解偏好就推荐，则可能导致匹配失败。我们提出对话即发现（D2D）框架，基于商品属性结构动态优化提问策略，自适应选择最具信息量的问题，并合理安排推荐时机，避免过早或偏离目标的推荐，从而提升互动体验。为评估D2D，我们从Amazon Reviews语料中构建了三个数据集。在基于多因素效用耐心模型的模拟对话中，D2D相比最先进基线，在目标商品发现准确率上提升22.2%-29.9%，会话放弃率降低6.6%-16.1%，平均对话时长减少27.5%。补充的用户研究进一步验证了其在用户满意度和感知效率上的显著提升。
原文摘要 · Abstract (English)
Conversational product search assistants offer a more expressive, natural, and interactive alternative to traditional keyword-based product search. With limited screen space, showing only a few items increases the need for precise preference elicitation, which can prolong conversations, leading to user frustration and session abandonment. Conversely, rushing to recommend items without a clear understanding of preferences risks poor matches and a degraded user experience. We present Dialogue to Discovery (D2D), an attribute-oriented preference elicitation framework that dynamically exploits the structure of product attributes to efficiently steer conversations toward the user's desired item. D2D adaptively prioritizes the most informative queries and strategically times product recommendations, reducing premature or off-target suggestions that harm engagement. To evaluate D2D, we curate three datasets from the Amazon Reviews corpus. In simulated conversations modelled using a multi-factor utilitarian patience framework, D2D achieves a 22.2-29.9% improvement in target-finding accuracy, 6.6-16.1% reduction in abandonment, and 27.5% shorter average conversations over the state-of-the-art baselines. A complementary user study further confirms significant gains in both user satisfaction and perceived efficiency.
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