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Special Topics in Data Analysis and Knowledge Discovery
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 2
368-374

User purchase intention prediction based on evolutionary ensemble learning

Zhang Yifana
Yu Qianchenga,b
Zhang Lisia
a. School of Computer Science & Engineering, b. The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750030, China
DOI: 10.19734/j.issn.1001-3695.2023.07.0272

Abstract

In the era of e-commerce, accurately predicting user purchase intentions has become a crucial factor for enhancing sales efficiency and optimizing the customer experience. Addressing the limitations of traditional ensemble strategies, which often suffer from subjective biases during the model design phase, this paper introduced an adaptive evolutionary ensemble learning model to predict user purchase intentions. This model adaptively selected the optimal base learners and meta-learners, incorporating both the predictive information from the base learners and the differential information between features to expand the feature dimensions, enhancing prediction accuracy. Moreover, to further refine the predictive capabilities of the model, this paper designed a binary adaptive differential evolution algorithm for feature selection, aiming to identify features that significantly influence the prediction outcome. Research results show that the binary adaptive differential evolution algorithm outperforms traditional optimization algorithms in global searches and feature selection. Compared to six common ensemble models and the DeepForest model, the proposed evolutionary ensemble model achieves a 2.76% and 2.72% increase in AUC value, respectively, and effectively mitigates the impacts of data imbalance.

Foundation Support

宁夏重点研发计划(引才专项)项目(2022YCZX0013)
宁夏重点研发计划(重点)项目(2023BDE02001)
银川市校企联合创新项目(2022XQZD009)
北方民族大学2022年校级科研平台《数字化农业赋能宁夏乡村振兴创新团队》项目(2022PT_S10)
“图像与智能信息处理创新团队”国家民委创新团队资助项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.07.0272
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 2
Section: Special Topics in Data Analysis and Knowledge Discovery
Pages: 368-374
Serial Number: 1001-3695(2024)02-007-0368-07

Publish History

[2023-11-01] Accepted Paper
[2024-02-05] Printed Article

Cite This Article

张一凡, 于千城, 张丽丝. 基于进化集成学习的用户购买意向预测 [J]. 计算机应用研究, 2024, 41 (2): 368-374. (Zhang Yifan, Yu Qiancheng, Zhang Lisi. User purchase intention prediction based on evolutionary ensemble learning [J]. Application Research of Computers, 2024, 41 (2): 368-374. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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