基于深度森林的用户购买行为预测模型

doi:10.11896/j.issn.1002-137X.2019.09.027

Abstract

Abstract: In recent years,online retail kept growing at a high speed.There exist redundant user behavior data in website.User’s behavior can embody user’s preference in the e-commerce platform.How to utilize user behavior to mine user preferences has become the focus of attention in academia and industry,and has formed a number of research results.The prediction methods of user behavior only aims at a certain type of user behavior,which is not able to reflect the overall characteristics of user behavior.Therefore,this paper proposed deep forest based prediction model of purchase behavior.By constructing feature engineering of user behavior,a whole user behavior feature model was built.In order to achieve efficient training,a deep forest based prediction method of purchase behavior was put forward to implement the behavior recognition training effect.The training time of this method is 43s,and the F1 value is 9.73%.Compared with other models,this method has achieved good results in both indexes.Finally,the experiments show that the model has an ability to reduce the time overhead and improve the prediction accuracy.

Key words: Characteristics of user behavior, Deep forest, Feature engineering, Prediction of purchase behavior

CLC Number:

TP181

GE Shao-lin, YE Jian, HE Ming-xiang. Prediction Model of User Purchase Behavior Based on Deep Forest[J].Computer Science, 2019, 46(9): 190-194.

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