摘要
【目的】基于用户网络评论构建有效的评分预测模型,挖掘用户消费行为特征。【方法】基于LDA模型,量化用户评论为主题特征向量作为解释变量,将用户评分作为被解释变量,采用XGBoost算法,并加入样本扰动和属性扰动生成多个模型进行集成,构建用户评分预测模型。【结果】针对某汽车门户网站的用户评论评分预测结果表明,该模型较好地揭示了用户对汽车商品的偏好。较逻辑回归、随机森林算法,其预测准确度分别高出13.73%、0.64%,且具有较高的计算效率。【局限】未融合其他方面的数据对用户行为特征进行更全面的刻画。【结论】将用户评论量化为主题特征向量,基于XGBoost算法能够准确、高效地预测用户评分。
[Objective] This study aims to build a model for effectively predicting ratings of user reviews and analysing consumer behaviours. [Methods] First, we applied the Latent Dirichlet Allocation model to set the topic features from user reviews as independent variable and user ratings as dependent variable. Then, we built a user rating prediction model based on the e Xtreme Gradient Boosting algorithm. Finally, we added the disturbances of samples and attributes to the proposed model for rating prediction. [Results] We used the new model to predict user's comments on a domestic automobile online portal, and identified their preferences of automobile. Compared with the Logical Regression and Random Forest algorithms, the proposed model has better precision and efficiency. [Limitations] We need to include data from other fields to more comprehensively describe user's behaviours. [Conclusions] The proposed model could quantify user's reviews and then predict their ratings effectively.
引文
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