论文标题
基于用户评论的电子商务基于查询的一代
E-commerce Query-based Generation based on User Review
论文作者
论文摘要
随着电子商务平台上越来越多的商品，用户倾向于参考其他购物者的评论来决定他们应该购买的产品。但是，有了如此众多的产品评论，用户通常必须花费大量时间来浏览评论，讨论他们不在乎的产品属性。我们想建立一个可以自动汇总和回答用户特定问题的系统。 在这项研究中，我们提出了一种基于SEQ2SEQ的新型文本生成模型，以根据以前用户发布的评论来生成对用户问题的答案。考虑到用户问题和/或目标情感极性，我们提取了感兴趣的各个方面，并产生一个答案，总结了以前的相关用户评论。具体而言，我们的模型在编码过程中在输入审查和目标方面之间表现出注意力，并在解码过程中的审查评级和输入上下文中进行了调整。我们还结合了预先培训的辅助评级分类器，以提高模型性能和在训练过程中加速收敛。使用现实世界电子商务数据集的实验表明，与先前介绍的模型相比，我们的模型可以提高性能。
With the increasing number of merchandise on e-commerce platforms, users tend to refer to reviews of other shoppers to decide which product they should buy. However, with so many reviews of a product, users often have to spend lots of time browsing through reviews talking about product attributes they do not care about. We want to establish a system that can automatically summarize and answer user's product specific questions. In this study, we propose a novel seq2seq based text generation model to generate answers to user's question based on reviews posted by previous users. Given a user question and/or target sentiment polarity, we extract aspects of interest and generate an answer that summarizes previous relevant user reviews. Specifically, our model performs attention between input reviews and target aspects during encoding and is conditioned on both review rating and input context during decoding. We also incorporate a pre-trained auxiliary rating classifier to improve model performance and accelerate convergence during training. Experiments using real-world e-commerce dataset show that our model achieves improvement in performance compared to previously introduced models.
