融合信息增益和梯度下降算法的在线评论有用程度预测模型

Helpfulness Degree Prediction Model of Online Reviews Fusing Information Gain and Gradient Decline Algorithms

由于无法预知产品在线评论的文本内容是否对浏览者有用,大量的无用评论增加了潜在消费者的信息搜索成本,甚至降低了潜在消费者购买产品的可能性。为提高电子商务平台的有用在线评论率,为撰写评论者提供测试功能,建立在线评论有用程度预测模型。根据在线评论的文本特征,所提模型选择在线评论的词语数量、词语的有用值、产品特征数量等3个特征,构建一个预测在线评论有用程度的模型,其中词语的有用值是词语区分在线评论有用程度的信息增益量,然后根据大量在线评论数据利用梯度下降算法解出模型参数。实验结果显示,随着词语数量、词语有用值、产品特征数量的增长,评论有用程度不断提高。实验中把在线评论分为一般、有用、非常有用3个程度,对于一般的在线评论,预测精确率为92.96%;对于“有用”在线评论,预测精确率为94.83%;对于“非常有用”在线评论,预测精确率为67.63%。实验对模型性能进行测试,得到平均精确率为85.05%,召回率为82.81%,F1值为83.72%,该结果验证了所提模型预测在线评论有用程度的可行性。

Because it is impossible to predict whether the text content of online product reviews is helpful for viewers,many reviewers write a large number of unhelpful reviews,which increases the cost of information search for potential consumers,and even reduces the possibility of potential consumers buying products.In order to improve the helpful online reviews rate of e-commerce platform and provide test function for reviewers,a prediction model of online reviews helpfulness is established.According to the text characteristics of online reviews,the model chooses three features of online reviews:the number of words,the helpful value of words,and the number of product features,to construct a model for predicting the helpfulness of online reviews.The helpful value is the information gain of words to distinguish the helpfulness of online reviews.And then,according to a large number of online reviews,by using the gradient descent algorithm,the model parameters are solved.The experimental results show that with the increase of the number of words,helpful value of words and the number of product features,the helpfulness of reviews increases continuously.The online reviews are divided into three levels:general,helpful and very helpful.The general predicted accuracy of online reviews is 92.96%,helpful accuracy is 94.83%,and very helpful accuracy is 67.63%.The average accuracy,recall and F1 of the model are 85.05%,82.81%and 83.72%,respectively.The results verify the feasibility of the model to predict the helpfulness of online reviews.