A Transformer-Based Approach for Improving App Review Response Generation

TL;DR

This paper presents TRRGen, a Transformer-based model that automatically generates app review responses, improving reply quality and outperforming existing methods, thus aiding developers in managing user feedback efficiently.

Contribution

The paper introduces TRRGen, a novel Transformer-based approach for automated app review response generation, leveraging review and app features to enhance reply quality.

Findings

TRRGen outperforms current state-of-the-art methods in response quality.

Generated replies are validated as effective through manual evaluation.

The approach significantly reduces the manual effort needed for review responses.

Abstract

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.