With the fast growth of e-commerce, more people choose to purchase products online and browse reviews before making decisions. It is essential to identify helpful reviews, given the typical large number of reviews and the various range of quality. In this paper, we aim to build a model to predict review helpfulness automatically. Our work is inspired by the observation that a customer’s expectation of a review can be greatly affected by review sentiment and the degree to which the customer is aware of pertinent product information. Consequently, a customer may pay more attention to that specific content of a review which contributes more to its helpfulness from their perspective. To model such customer expectations and capture important information from a review text, we propose a novel neural network which leverages review sentiment and product information. Specifically, we encode the sentiment of a review through an attention module, to get sentiment-driven information from review text. We also introduce a product attention layer that fuses information from both the target product and related products, in order to capture the product related information from review text. Our experimental results for the task of identifying whether a review is helpful or not show an AUC improvement of 5.4% and 1.5% over the previous state of the art model on Amazon and Yelp data sets, respectively. We further validate the effectiveness of each attention layer of our model in two application scenarios. The results demonstrate that both attention layers contribute to the model performance, and the combination of them has a synergistic effect. We also evaluate our model performance as a recommender system using three commonly used metrics: NDCG@10, Precision@10 and Recall@10. Our model outperforms PRH-Net, a state-of-the-art model, on all three of these metrics.

随着电子商务的快速发展，越来越多的人选择在线购买产品并浏览评论，然后再做出决定。鉴于典型的大量评论和各种质量范围，识别有用的评论至关重要。在本文中，我们旨在建立一个模型来自动预测评论的有用性。我们的工作受到以下观察的启发，即观察的情绪和顾客对相关产品信息的了解程度会极大地影响顾客对评论的期望。因此，客户可能会更多地关注评论的特定内容，从他们的角度来看，评论的特定内容会有所帮助。为了建模此类客户期望并从评论文本中获取重要信息，我们提出了一种利用评论情绪和产品信息的新型神经网络。具体来说，我们通过注意力模块对评论的情感进行编码，以从评论文本中获取情感驱动的信息。我们还引入了一个产品关注层，该层融合了来自目标产品和相关产品的信息，以便从评论文本中捕获与产品相关的信息。我们用于确定评论是否有帮助的实验结果显示，与先前在Amazon和Yelp数据集上的最新模型相比，AUC分别提高了5.4％和1.5％。我们进一步验证了模型在两个应用场景中每个关注层的有效性。结果表明，两个注意层都对模型性能有所贡献，它们的组合具有协同作用。我们还使用三个常用指标评估了作为推荐系统的模型性能：NDCG @ 10，Precision @ 10和Recall @ 10。在所有这三个指标上，我们的模型均优于最新模型PRH-Net。