基于嵌入门控CNN的帮助度预测
Review Helpfulness Prediction with Embedding-Gated CNN
摘要：
产品评论以文本形式为主，对消费者最终做出购买决定有着重要的帮助。因此，电子商务公司预测评论有用性以更翔实的方式呈现和推荐评论是很重要的。在这项工作中，我们引入了一个卷积神经网络模型，能够提取多粒度表示的抽象特征。受不同单词对句子意义的贡献不同这一事实的启发，我们考虑为所有表示学习单词级嵌入门。此外，由于某些产品领域/类别通常具有丰富的用户评论，因此其他领域没有。为了帮助数据不足的领域，我们将我们的模型集成到一个跨领域的关系学习框架中，以有效地从其他领域转移知识。大量的实验表明，我们的模型产生更好的性能比现有的方法。
英文摘要：
Product reviews, in the form of texts dominantly, significantly help consumers finalize their purchasing decisions. Thus, it is important for e-commerce companies to predict review helpfulness to present and recommend reviews in a more informative manner. In this work, we introduce a convolutional neural network model that is able to extract abstract features from multi-granularity representations. Inspired by the fact that different words contribute to the meaning of a sentence differently, we consider to learn word-level embedding-gates for all the representations. Furthermore, as it is common that some product domains/categories have rich user reviews, other domains not. To help domains with less sufficient data, we integrate our model into a cross-domain relationship learning framework for effectively transferring knowledge from other domains. Extensive experiments show that our model yields better performance than the existing methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
