Recent advances in natural language processing and deep learning have accelerated the development of digital assistants. In conversational commerce, these assistants help customers find suitable products in online shops through natural language conversations. During the dialogue, the assistant identifies the customer's needs and preferences and subsequently suggests potentially relevant products. Traditional online shops often allow users to filter search results based on their preferences using facets. Selected facets can also serve as a reminder of how the product base was filtered. In conversational commerce, however, the absence of facets and the use of advanced natural language processing techniques can leave customers uncertain about how their input was processed by the system. This can hinder transparency and trust, which are critical factors influencing customers' purchase intentions. To address this issue, we propose a novel text-based digital assistant that, in the product assessment step, explains how specific product aspects relate to the user's previous utterances to enhance transparency and facilitate informed decision-making. We conducted a user study (N=135) and found a significant increase in user-perceived transparency when natural language explanations and highlighted text passages were provided, demonstrating their potential to extend system transparency to the product assessment step in conversational commerce.

翻译：自然语言处理与深度学习的近期进展加速了数字助手的发展。在对话式商务场景中，这些助手通过自然语言对话帮助顾客在在线商店中寻找合适商品。对话过程中，助手识别顾客的需求与偏好，随后推荐潜在相关商品。传统在线商店通常允许用户通过分面导航根据偏好筛选搜索结果，已选分面也可作为商品库筛选依据的提示。然而在对话式商务中，分面导航的缺失与先进自然语言处理技术的应用，可能导致顾客无法确知系统如何处理其输入信息。这会削弱系统透明度与用户信任，而这两者是影响顾客购买意愿的关键因素。为解决该问题，我们提出一种新型文本数字助手，在商品评估步骤中解释特定商品属性如何关联用户先前话语，以增强透明度并促进知情决策。我们开展了一项用户研究（N=135），发现当提供自然语言解释并高亮文本段落时，用户感知透明度显著提升，这证明该方法具备将系统透明度延伸至对话式商务商品评估步骤的潜力。