基于Cross-DeepFM的军事训练推荐模型  

A military training recommendation model based on Cross-DeepFM

作　　者：高永强 张之明[1] 王宇涛 GAO Yong-qiang;ZHANG Zhi-ming;WANG Yu-tao(College of Information Engineering,Engineering University of the Chinese People’s Armed Police Force,Xi’an 710086;Guizhou Provincial Corps,the Chinese People’s Armed Police Force,Guiyang 550081,China)

机构地区：[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

出　　处：《计算机工程与科学》2022年第8期1364-1371,共8页Computer Engineering & Science

基　　金：军内科研项目基金(WJ2020A020003);军事理论课题基金(WJJY21JL0286)。

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。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.

关 键 词：军事训练 推荐算法 深度学习 因子分解机 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]