A deep neural network-enhanced pairwise bilinear factorization machine model
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 comprehen-sively consider high-order interaction features and original low-order features when modeling the interac-tions 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 sys-tem but also improve the prediction performance of the model.Experiments conducted on two real-world datasets show that the proposed model achieves higher recommendation accuracy and outperforms other models in objective metrics such as HR and NDCG.
万方数据
维普
