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The neural network-enhanced factorization machine (FM) model, which can capture more high-order feature interactions and improve the accuracy of predictions, has become a research hotspot in the field of recommendation algorithms. Aiming at the problem that existing models do not comprehensively consider high-order interaction features and original low-order features when modeling the interactions between users and items, and in order to improve the model's ability to model user preferences, this paper proposes a new deep neural network-enhanced pairwise bilinear factorization machine model, DeepPRBFM, by combining depth neural networks and pairwise learning. This model adopts a bilinear structure with a pair of inputs containing positive and negative samples, utilizes multi-layer ResNet to preserve low-order features, enhances the interaction of high-order features with DNN, and employs a pairwise ranking-based loss function. Moreover, in the bilinear structure, increasing the proportion of negative samples can not only significantly alleviate the cold start problem of the recommendation system but also improve the prediction performance of the model. Experiments conducted on two realworld datasets show that the proposed model achieves higher recommendation accuracy and outperforms other models in objective metrics such as HR and NDCG. [ABSTRACT FROM AUTHOR]

SetRank is a novel Setwise Bayesian collaborative ranking model that can model implicit feedback data more closely to real-world scenarios. However, SetRank only considers collaborative information and lacks effective use of content information. In order to solve the above problems, a factorization machine model based on Setwise ranking, named SRFMs, is proposed. Drawing on the Setwise ranking proposed in SetRank to solve the problem of missing negative samples in implicit feedback, a factorization machine is chosen as a predictor to model content information, and model user preferences from the perspective of optimal item ranking. Furthermore, to improve the fixed feature representation and the lack of higher-order feature interactions in standard FM, inspired by IFM, SR-DIFM model is constructed to incorporate the SRFMs by combining input-aware networks with neural networks. Experimental results on two real-world datasets demonstrate that the proposed model outperforms the state-of-art model in terms of evaluation metrics including HR, NDCG and mAP, and can improve the accuracy of recommendations which make better use of user and item content information while solving the recommendation problem under implicit feedback. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]