  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.
