计算机科学 ›› 2019, Vol. 46 ›› Issue (9): 190-194.doi: 10.11896/j.issn.1002-137X.2019.09.027

• 人工智能 • 上一篇    下一篇

基于深度森林的用户购买行为预测模型

葛绍林1,3, 叶剑2,3, 何明祥1   

  1. (山东科技大学计算机科学与工程学院 山东 青岛266590)1;
    (中国科学院计算技术研究所泛在计算系统研究中心 北京100190)2;
    (移动计算与新型终端北京市重点实验室 北京100190)3
  • 收稿日期:2018-07-27 出版日期:2019-09-15 发布日期:2019-09-02
  • 通讯作者: 何明祥(1969-),男,博士,副教授,硕士生导师,主要研究方向为信息处理、人工智能,E-mail:hmx0708@163.com
  • 作者简介:葛绍林(1993-),男,硕士生,主要研究方向为数据挖掘;叶 剑(1974-),男,高级工程师,主要研究方向为普适计算、数据挖掘;
  • 基金资助:
    国家重点研发计划项目(2016YFB1001100),国家自然科学青年基金课题(61401040)

Prediction Model of User Purchase Behavior Based on Deep Forest

GE Shao-lin1,3, YE Jian2,3, HE Ming-xiang1   

  1. (College of Computer Science and Engineering,Shandong University of Science and Technology,Qingdao,Shandong 266590,China)1;
    (Research Center for Ubiquitous Computing Systems,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China)2;
    (The Beijing Key Laboratory of Mobile Computing and Pervasive Device,Beijing 100190,China)3
  • Received:2018-07-27 Online:2019-09-15 Published:2019-09-02

摘要: 近年来,网络零售保持高速增长,网站中富含大量的用户行为数据。电商平台中的用户对商品的操作行为可以体现用户偏好,如何利用用户行为挖掘用户偏好已经成为学术界和工业界的关注焦点,并已经取得了众多研究成果。然而,目前用户操作行为预测方法研究通常只针对用户某一类操作行为进行分析,无法完备反映用户行为的整体特征。因此,提出一种基于深度森林的用户购买行为预测模型,通过构建用户行为特征工程建立整体用户行为特征模型;基于此,提出基于深度森林的用户购买行为预测方法,实现高效的行为预测训练效果。该方法的训练时间为43s,F1值为9.73%,相对其他模型取得了更好的效果。实验结果表明,该模型在降低时间开销的同时,提高了预测准确率。

关键词: 购买行为预测, 深度森林, 特征工程, 用户行为特征

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

中图分类号: 

  • TP181
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