Previous studies have demonstrated that proactive interaction with user reviews has a positive impact on the perception of app users and encourages them to submit revised ratings. Nevertheless, developers encounter challenges in managing a high volume of reviews, particularly in the case of popular apps with a substantial influx of daily reviews. Consequently, there is a demand for automated solutions aimed at streamlining the process of responding to user reviews. To address this, we have developed a new system for generating automatic responses by leveraging user-contributed documents with the help of retrieval-augmented generation (RAG) and advanced Large Language Models (LLMs). Our solution, named SCRABLE, represents an adaptive customer review response automation that enhances itself with self-optimizing prompts and a judging mechanism based on LLMs. Additionally, we introduce an automatic scoring mechanism that mimics the role of a human evaluator to assess the quality of responses generated in customer review domains. Extensive experiments and analyses conducted on real-world datasets reveal that our method is effective in producing high-quality responses, yielding improvement of more than 8.5% compared to the baseline. Further validation through manual examination of the generated responses underscores the efficacy our proposed system.

翻译：先前研究表明，主动与用户评论互动能积极影响应用用户感知，并鼓励其提交修正后的评分。然而，开发者面临管理大量评论的挑战，尤其是对于每日评论量巨大的热门应用而言。因此，亟需能够简化用户评论回复流程的自动化解决方案。为此，我们利用检索增强生成（RAG）技术和先进大语言模型（LLMs），基于用户贡献文档开发了一套新型自动回复生成系统。我们的解决方案名为SCRABLE，是一种自适应客户评论回复自动化系统，通过自优化提示和基于LLM的判断机制实现自我增强。此外，我们引入了一种可自动模拟人类评估者角色的评分机制，用于评估客户评论领域生成回复的质量。在真实数据集上开展的大量实验与分析表明，该方法能有效生成高质量回复，相较基准方法提升超过8.5%。通过人工检查生成回复的进一步验证，也充分证明了我们提出系统的有效性。