# Automating App Review Response Generation

arXiv: https://arxiv.org/abs/2002.03552 · HTML: https://mlsanity.com/paper/2002.03552/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2020.

## Built on by (most cited first, 8 of 8)

- [MobileRec: A Large-Scale Dataset for Mobile Apps Recommendation](https://mlsanity.com/paper/2303.06588/) (2023)
- [Self-Improving Customer Review Response Generation Based on LLMs](https://mlsanity.com/paper/2405.03845/) (2024)
- [Proactive Prioritization of App Issues via Contrastive Learning](https://mlsanity.com/paper/2303.06586/) (2023)
- [Pre-Trained Neural Language Models for Automatic Mobile App User Feedback Answer Generation](https://mlsanity.com/paper/2202.02294/) (2022)
- [App Review Driven Collaborative Bug Finding](https://mlsanity.com/paper/2301.02818/) (2023)
- [Generating Persuasive Responses to Customer Reviews with Multi-Source Prior Knowledge in E-commerce](https://mlsanity.com/paper/2209.09497/) (2022)
- [What Users Value and Critique: Large-Scale Analysis of User Feedback on AI-Powered Mobile Apps](https://mlsanity.com/paper/2506.10785/) (2025)
- [PARAN: Persona-Augmented Review ANswering system on Food Delivery Review Dataset](https://mlsanity.com/paper/2512.10148/) (2025)

## Abstract (arXiv)

Previous studies showed that replying to a user review usually has a positive effect on the rating that is given by the user to the app. For example, Hassan et al. found that responding to a review increases the chances of a user updating their given rating by up to six times compared to not responding. To alleviate the labor burden in replying to the bulk of user reviews, developers usually adopt a template-based strategy where the templates can express appreciation for using the app or mention the company email address for users to follow up. However, reading a large number of user reviews every day is not an easy task for developers. Thus, there is a need for more automation to help developers respond to user reviews. Addressing the aforementioned need, in this work we propose a novel approach RRGen that automatically generates review responses by learning knowledge relations between reviews and their responses. RRGen explicitly incorporates review attributes, such as user rating and review length, and learns the relations between reviews and corresponding responses in a supervised way from the available training data. Experiments on 58 apps and 309,246 review-response pairs highlight that RRGen outperforms the baselines by at least 67.4% in terms of BLEU-4 (an accuracy measure that is widely used to evaluate dialogue response generation systems). Qualitative analysis also confirms the effectiveness of RRGen in generating relevant and accurate responses.