User Purchase Intention Prediction Based on Improved Deep Forest  

作　　者：Yifan Zhang Qiancheng Yu Lisi Zhang 

机构地区：[1]School of Computer Science and Engineering,North Minzu University,Yinchuan,750030,China [2]Laboratory of Graphics and Images of the State Ethnic Affairs Commission,North Minzu University,Yinchuan,750030,China

出　　处：《Computer Modeling in Engineering & Sciences》2024年第4期661-677,共17页工程与科学中的计算机建模（英文）

基　　金：supported by Ningxia Key R&D Program (Key)Project (2023BDE02001);Ningxia Key R&D Program (Talent Introduction Special)Project (2022YCZX0013);North Minzu University 2022 School-Level Research Platform“Digital Agriculture Empowering Ningxia Rural Revitalization Innovation Team”,Project Number:2022PT_S10;Yinchuan City School-Enterprise Joint Innovation Project (2022XQZD009);“Innovation Team for Imaging and Intelligent Information Processing”of the National Ethnic Affairs Commission.

摘　　要：Widely used deep neural networks currently face limitations in achieving optimal performance for purchase intention prediction due to constraints on data volume and hyperparameter selection.To address this issue,based on the deep forest algorithm and further integrating evolutionary ensemble learning methods,this paper proposes a novel Deep Adaptive Evolutionary Ensemble(DAEE)model.This model introduces model diversity into the cascade layer,allowing it to adaptively adjust its structure to accommodate complex and evolving purchasing behavior patterns.Moreover,this paper optimizes the methods of obtaining feature vectors,enhancement vectors,and prediction results within the deep forest algorithm to enhance the model’s predictive accuracy.Results demonstrate that the improved deep forest model not only possesses higher robustness but also shows an increase of 5.02%in AUC value compared to the baseline model.Furthermore,its training runtime speed is 6 times faster than that of deep models,and compared to other improved models,its accuracy has been enhanced by 0.9%.

关 键 词：Purchase prediction deep forest differential evolution algorithm evolutionary ensemble learning model selection 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]