基于Cross-DeepFM的军事训练推荐模型

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。

A military training recommendation modelbased on Cross-DeepFM

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.