Mobile apps are becoming an integral part of people's daily life by providing various functionalities, such as messaging and gaming. App developers try their best to ensure user experience during app development and maintenance to improve the rating of their apps on app platforms and attract more user downloads. Previous studies indicated that responding to users' reviews tends to change their attitude towards the apps positively. Users who have been replied are more likely to update the given ratings. However, reading and responding to every user review is not an easy task for developers since it is common for popular apps to receive tons of reviews every day. Thus, automation tools for review replying are needed. To address the need above, the paper introduces a Transformer-based approach, named TRRGen, to automatically generate responses to given user reviews. TRRGen extracts apps' categories, rating, and review text as the input features. By adapting a Transformer-based model, TRRGen can generate appropriate replies for new reviews. Comprehensive experiments and analysis on the real-world datasets indicate that the proposed approach can generate high-quality replies for users' reviews and significantly outperform current state-of-art approaches on the task. The manual validation results on the generated replies further demonstrate the effectiveness of the proposed approach.
翻译:通过提供信息和游戏等各种功能,移动应用程序正在成为人们日常生活不可分割的一部分。 App 开发者在开发和维护应用过程中尽力确保用户在开发和维护过程中的经验,以提高应用程序在应用程序平台上的评级,吸引更多的用户下载。先前的研究显示,对用户审查的回应往往会积极改变其对应用程序的态度。已作出答复的用户更可能更新给定评级。然而,阅读和回复每一次用户审查对开发者来说并不是一项容易的任务,因为大众应用程序每天接收大量审查是常见的。因此,需要自动化审查工具来答复。为满足上述需要,该文件采用了以变换器为基础的方法,名为TRRGen,以自动生成对特定用户审查的回应。TRRGen 提取应用程序的类别、评级和审查文本作为输入特征。通过修改以变换器为基础的模型,TRRGen能够产生对新审查的适当答复。关于真实世界数据集的全面试验和分析表明,拟议方法可以为用户提供高质量的答复,并大大超出当前工作成效验证方法。