论文标题
上下文感知的在线产品评论的有用预测
Context-aware Helpfulness Prediction for Online Product Reviews
论文作者
论文摘要
由于电子商务网站和在线商店的扩散，对审查有益的建模和预测有用的乐趣变得更加多。由于购买之前无法测试产品的功能，因此人们通常会依靠各种用户评论来决定是否购买产品。但是，质量评论可能被深入埋葬在大量评论的堆中。因此，根据审核质量向客户推荐评论是本质上的。由于没有直接指示审核质量的指示，因此大多数评论都使用“ X Out y”用户的信息发现评论有助于获得审核质量。但是，这种方法破坏了有益的预测，因为并非所有评论都具有统计上丰富的选票。在本文中，我们提出了一个神经深度学习模型，该模型可以预测评论的有益性评分。该模型基于卷积神经网络（CNN）和上下文感知的编码机制，该机制可以直接捕获单词之间的关系，而不论其长期距离的距离如何。我们在人类注释的数据集上验证了模型，结果表明，我们的模型明显优于现有模型以进行有益的预测。
Modeling and prediction of review helpfulness has become more predominant due to proliferation of e-commerce websites and online shops. Since the functionality of a product cannot be tested before buying, people often rely on different kinds of user reviews to decide whether or not to buy a product. However, quality reviews might be buried deep in the heap of a large amount of reviews. Therefore, recommending reviews to customers based on the review quality is of the essence. Since there is no direct indication of review quality, most reviews use the information that ''X out of Y'' users found the review helpful for obtaining the review quality. However, this approach undermines helpfulness prediction because not all reviews have statistically abundant votes. In this paper, we propose a neural deep learning model that predicts the helpfulness score of a review. This model is based on convolutional neural network (CNN) and a context-aware encoding mechanism which can directly capture relationships between words irrespective of their distance in a long sequence. We validated our model on human annotated dataset and the result shows that our model significantly outperforms existing models for helpfulness prediction.
