论文标题
具有电子商务的多源先验知识的对客户评论的有说服力的回应
Generating Persuasive Responses to Customer Reviews with Multi-Source Prior Knowledge in E-commerce
论文作者
论文摘要
客户评论通常包含有关一个人在线购物体验的大量信息。尽管积极的评论对商店有益，但负面评论将在很大程度上影响消费者的决定，并可能导致销售下降。因此，仔细和有说服力地回答每次负面审查并最大程度地减少其不利影响至关重要。最近的研究考虑利用生成模型来帮助卖家做出回应。但是，这个问题并没有得到充分解决，因为评论可能包含问题的多个方面，这些方面应相应和有说服力地解决。在这项工作中，我们为有说服力的响应生成提出了一个多源多相关生成模型。提出的模型适当地获得和利用了各种信息来源，以产生更有信息和有说服力的响应。提出了一个多方面的细心网络，以自动参与审查中的不同方面，并确保解决大多数问题。在两个现实世界数据集上进行的广泛实验表明，我们的方法优于最先进的方法和在线测试，这证明我们部署的系统大大提高了商店处理负面评论的效率。
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.
