具有情感意识的对话推荐系统：一个案例研究
Emotion-Aware Conversational Recommender Systems: a Case Study
AI总结：
本研究提出了一种具有情感意识的对话推荐系统，通过分析用户情绪提升在线购物体验，采用基于内容的推荐方法，结合自然语言处理实现个性化建议。
摘要：
近年来，电子商务迅速发展，尤其是在新冠疫情时期。然而，它仍然缺乏实体店的典型元素，如 empathic 支持和销售员的个性化建议。本研究探讨了如何通过一种具有情感意识的对话代理（CA）来改善在线购物体验，使其能够以更自然和人性化的方式回应用户情绪。该项目聚焦于 Gala，一个为 Galeries Lafayette 网站开发的虚拟助手，能够从语音信息中识别情绪状态并相应地调整其回应。通过半结构化访谈分析用户需求，从而指导 Gala 的用户体验和功能设计。Gala 使用 OpenAI API 和 Galeries Lafayette API 实现，采用基于内容的推荐方法。通过自然语言处理，它解读用户请求并检索与特定属性（如名称、价格和品牌）相匹配的产品，从而实现流畅的对话和定制化的建议。进行了两项用户研究：一项是可用性测试，另一项是标准 CA 与 Gala 情感意识版本之间的比较评估。结果突显了具有情感意识的 CA 在使在线购物更快、更吸引人和更接近实体店引导体验方面的潜力。
英文摘要：
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.
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。
