In recent years, online shopping has grown rapidly, especially during the COVID-19 period. However, it still lacks elements typical of physical stores, such as empathic support and personalised advice from a sales assistant. This study explores how an emotion-aware Conversational Agent (CA) can improve the online shopping experience by responding to user emotions in a more natural and human way. The project focuses on Gala, a virtual assistant developed for the Galeries Lafayette website, capable of recognising emotional states from voice messages and adapting its responses accordingly. User needs were first analysed through semi-structured interviews, which informed the design of Gala's UX and functionalities. Gala was implemented using the OpenAI API and the Galeries Lafayette API, adopting a Content-Based recommendation approach. Through Natural Language Processing, it interprets user requests and retrieves products aligned with specific attributes such as name, price, and brand, enabling fluid dialogue and tailored suggestions. Two user studies were conducted: a usability test and a comparative evaluation between a standard CA and Gala's emotion-aware version. The results highlight the potential of emotion-aware CAs to make online shopping faster, more engaging, and closer to an in-store guided experience.

翻译：近年来，在线购物发展迅速，尤其是在COVID-19疫情期间。然而，它仍然缺乏实体店的典型元素，例如来自销售助理的共情支持和个性化建议。本研究探讨了情感感知对话代理（CA）如何通过以更自然、更人性化的方式响应用户情绪来改善在线购物体验。该项目聚焦于Gala，这是一个为老佛爷百货网站开发的虚拟助手，能够从语音消息中识别情绪状态并相应调整其回应。首先通过半结构化访谈分析了用户需求，这为Gala的用户体验和功能设计提供了依据。Gala使用OpenAI API和老佛爷百货API实现，采用了基于内容的推荐方法。通过自然语言处理，它解析用户请求并检索与特定属性（如名称、价格和品牌）匹配的产品，从而实现流畅的对话和定制化建议。进行了两项用户研究：一项可用性测试，以及标准CA与Gala情感感知版本之间的比较评估。结果突显了情感感知CA在使在线购物更快、更具吸引力、更接近店内引导体验方面的潜力。