Generating Persuasive Responses to Customer Reviews with Multi-Source Prior Knowledge in E-commerce
This addresses the challenge for e-commerce sellers in managing negative reviews to minimize sales impact, though it appears incremental as it builds on existing generation models with multi-source and multi-aspect improvements.
The paper tackled the problem of generating persuasive responses to negative customer reviews in e-commerce by proposing a Multi-Source Multi-Aspect Attentive Generation model, which outperformed state-of-the-art methods in experiments on two real-world datasets and enhanced efficiency in online tests.
Customer reviews usually contain much information about one's online shopping experience. While positive reviews are beneficial to the stores, negative ones will largely influence consumers' decision and may lead to a decline in sales. Therefore, it is of vital importance to carefully and persuasively reply to each negative review and minimize its disadvantageous effect. Recent studies consider leveraging generation models to help the sellers respond. However, this problem is not well-addressed as the reviews may contain multiple aspects of issues which should be resolved accordingly and persuasively. In this work, we propose a Multi-Source Multi-Aspect Attentive Generation model for persuasive response generation. Various sources of information are appropriately obtained and leveraged by the proposed model for generating more informative and persuasive responses. A multi-aspect attentive network is proposed to automatically attend to different aspects in a review and ensure most of the issues are tackled. Extensive experiments on two real-world datasets, demonstrate that our approach outperforms the state-of-the-art methods and online tests prove that our deployed system significantly enhances the efficiency of the stores' dealing with negative reviews.