基于文本信息和层次神经网络的产品评分方法

摘要/Abstract

摘要： 通常点评网站对商品的打分都是通过对商品评论的评分求均值而获得,但是这种方式严重依赖于评论的评分,而且对于评论数较少的商品,这种方式显得不够精确。不同于传统的产品打分机制,文中提出了一种根据产品评论的文本信息对产品进行整体打分的层次神经网络模型,该模型可以从有限的评论中分析出产品较为公正的得分。在产品评论中,存在着[词-句子-评论-商品]的层级结构,因此采用了三层GRU的结构分别来对句子、评论、商品进行表示,从而预测商品最终的打分。除此之外,还对评论层进行了额外地输出,进一步提高了商品得分预测的准确率。在回归和分类两种预测任务上的实验结果表明,模型的层次结构对于预测商品得分具有至关重要的作用,同时输出评论的得分可以进一步提高预测的准确率。

关键词: 层次神经网络, 产品打分, 评分预测

Abstract: Usually,the rating of the product on the website is obtained by averaging the rating of the product review,but this method relies heavily on the rating of reviews,which is not accurate enough for products with fewer reviews.Different from the traditional product scoring mechanism,this paper proposed a hierarchical neural network model for the overall scoring of products based on the text information of them,which can analyze the fair scores of products from limited reviews.In the product review,there is a hierarchical structure of [word-sentence-review-product],so the structure of three-layer GRU is used to get the representations of the sentences,reviews and products separately,so as to predict the final score of the product.In addition,this paper also makes additional output to the review layer to further improve the accuracy of the prediction.Experiments on the two prediction tasks of regression and classification show that the hierarchical structure of the model plays a crucial role in predicting the score of the product,and the score of outputting comment can further improve the prediction accuracy.

Key words: Hierarchical neural network, Product rating, Rating prediction

中图分类号:

TP391

赵赟, 王中卿, 李寿山. 基于文本信息和层次神经网络的产品评分方法[J]. 计算机科学, 2019, 46(11A): 33-37. https://doi.org/

ZHAO Yun, WANG Zhong-qing, LI Shou-shan. Product Rating with Text Information and Hierarchical Neural Network[J]. Computer Science, 2019, 46(11A): 33-37. https://doi.org/

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