用大模型分析竞品用户评价，自动提出功能优化建议。
LLM-Cure: LLM-based Competitor User Review Analysis for Feature Enhancement
- 基于大模型识别评论中的功能点，对比竞品高分评价生成改进建议。
- 在70个安卓应用105万条评论上测试，准确率比现有方法最高提升13%。
- 建议实现率达73%，适合应用开发团队快速响应用户需求。
移动应用市场迅猛增长，持续创新与快速响应用户需求至关重要。用户满意度是应用成功的关键，开发者通常依赖用户评论（含评分与文字）识别改进方向。然而评论量庞大，手动分析困难，现有自动化方法或仅分析自身应用评论，忽视竞品对比，或无法提供具体优化建议。为此，我们提出基于大语言模型的竞品用户评论分析框架LLM-Cure，利用大模型自动为应用功能改进生成建议。具体而言，LLM-Cure通过大模型识别并分类评论中的功能点；当检测到用户投诉时，自动提取相关竞品中高分（4-5星）评论，生成适配目标应用的优化建议。我们在70个热门Android应用的1,056,739条评论上评估了LLM-Cure，结果表明其在功能标注任务上相比最先进方法，F1值最高提升13%，召回率最高提升16%，精确率最高提升11%。此外，我们通过应用更新日志验证建议可行性，发现73%的建议被实际采纳实现。
原文摘要 · Abstract (English)
The exponential growth of the mobile app market underscores the importance of constant innovation and rapid response to user demands. As user satisfaction is paramount to the success of a mobile application (app), developers typically rely on user reviews, which represent user feedback that includes ratings and comments to identify areas for improvement. However, the sheer volume of user reviews poses challenges in manual analysis, necessitating automated approaches. Existing automated approaches either analyze only the target apps reviews, neglecting the comparison of similar features to competitors or fail to provide suggestions for feature enhancement. To address these gaps, we propose a Large Language Model (LLM)-based Competitive User Review Analysis for Feature Enhancement) (LLM-Cure), an approach powered by LLMs to automatically generate suggestion s for mobile app feature improvements. More specifically, LLM-Cure identifies and categorizes features within reviews by applying LLMs. When provided with a complaint in a user review, LLM-Cure curates highly rated (4 and 5 stars) reviews in competing apps related to the complaint and proposes potential improvements tailored to the target application. We evaluate LLM-Cure on 1,056,739 reviews of 70 popular Android apps. Our evaluation demonstrates that LLM-Cure significantly outperforms the state-of-the-art approaches in assigning features to reviews by up to 13% in F1-score, up to 16% in recall and up to 11% in precision. Additionally, LLM-Cure demonstrates its capability to provide suggestions for resolving user complaints. We verify the suggestions using the release notes that reflect the changes of features in the target mobile app. LLM-Cure achieves a promising average of 73% of the implementation of the provided suggestions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。