• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学 ›› 2022, Vol. 44 ›› Issue (08): 1364-1371.

• 高性能计算 • 上一篇    下一篇

基于Cross-DeepFM的军事训练推荐模型

高永强1,张之明1,王宇涛2   

  1. (1.武警工程大学信息工程学院,陕西 西安 710086;2.中国人民武装警察部队贵州省总队,贵州 贵阳 550081)
  • 收稿日期:2021-10-13 修回日期:2021-12-20 接受日期:2022-08-25 出版日期:2022-08-25 发布日期:2022-08-25
  • 基金资助:
    军内科研项目基金(WJ2020A020003);军事理论课题基金(WJJY21JL0286)

A military training recommendation model based on Cross-DeepFM

GAO Yong-qiang1,ZHANG Zhi-ming1,WANG Yu-tao2   

  1. (1.College of Information Engineering,Engineering University of 
    the Chinese People’s Armed Police Force,Xi’an 710086;
    2.Guizhou Provincial Corps,the Chinese People’s Armed Police Force,Guiyang 550081,China)
  • Received:2021-10-13 Revised:2021-12-20 Accepted:2022-08-25 Online:2022-08-25 Published:2022-08-25

摘要: 为了将推荐系统应用到军事训练领域,充分发挥军事训练大数据在个性化训练方面的价值,提出了一种基于深度学习的混合推荐模型Cross-DeepFM。首先采集和预处理真实军事训练数据,构建出自定义军事训练数据集;然后将深度残差神经网络、深度交叉网络和因子分解机相结合,设计了Cross-DeepFM模型结构并对模型细节进行分析;最后在自定义军事训练数据集上进行了实验与分析比较。实验结果表明,该模型与主流推荐模型相比具有更高的准确度,可有效完成军事训练个性化推荐任务。

关键词: 军事训练, 推荐算法, 深度学习, 因子分解机

Abstract:  In order to apply the recommendation system to the field of military training and give full play to the value of military training big data in personalized training, a hybrid recommendation model called Cross-DeepFM is proposed. Firstly, the real military training data are collected and preprocessed, and the custom military training dataset is constructed. Then, the structure of the Cross-DeepFM model is designed by combining the deep residual neural network, deep cross network and factorization machine, and the details of the model are analyzed. Finally, the comparison and analysis are carried out on the custom military training data set. The experimental results show that the proposed model is more accurate than the mainstream recommendation model and can effectively complete the personalized recommendation task of military training.

Key words: military training, recommendation algorithm, deep learning, factorization machine