  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.
