User reviews on e-commerce and review sites are crucial for making purchase decisions, although creating detailed reviews is time-consuming and labor-intensive. In this study, we propose a novel use of dialogue systems to facilitate user review creation by generating reviews from information gathered during interview dialogues with users. To validate our approach, we implemented our system using GPT-4 and conducted comparative experiments from the perspectives of system users and review readers. The results indicate that participants who used our system rated their interactions positively. Additionally, reviews generated by our system required less editing to achieve user satisfaction compared to those by the baseline. We also evaluated the reviews from the reader' perspective and found that our system-generated reviews are more helpful than those written by humans. Despite challenges with the fluency of the generated reviews, our method offers a promising new approach to review writing.

翻译：尽管撰写详尽评论耗时费力，但电子商务与评论网站上的用户评论对购买决策至关重要。本研究提出一种利用对话系统辅助用户评论生成的新方法：通过访谈式对话收集用户信息并自动生成评论。为验证该方法的有效性，我们基于GPT-4构建了原型系统，并从系统使用者和评论阅读者双重视角开展了对比实验。结果表明：使用本系统的参与者对交互体验给予积极评价；相较于基线系统，本系统生成的评论需要更少的编辑即可满足用户需求。从读者视角评估发现，系统生成的评论比人工撰写的评论更具参考价值。尽管生成评论的流畅性仍存在挑战，但本方法为评论撰写提供了一种前景广阔的新途径。