神经网络增强的成对双线性因子分解机  

A deep neural network-enhanced pairwise bilinear factorization machine model

作　　者：周祺 周宁宁[1] ZHOU Qi;ZHOU Ning-ning(School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China)

机构地区：[1]南京邮电大学计算机学院,江苏南京210023

出　　处：《计算机工程与科学》2024年第9期1648-1659,共12页Computer Engineering & Science

摘　　要：基于神经网络增强的因子分解机模型因可以捕捉更多高阶特征的交互,使预测结果愈加精准而成为了当前推荐算法的研究热点。针对现有模型在对用户与物品交互特征进行建模时,并没有综合考虑高阶交互特征和原始低阶特征的问题,同时为了提高模型对用户偏好的建模能力,采用深度神经网络,并且结合成对学习提出了新的深度神经网络增强的成对双线性因子分解机模型DeepPRBFM。该模型采用一对分别包含正样本和负样本输入的双线性结构,利用多层ResNet保留低阶特征,利用DNN增强高阶特征的交互,并采用了基于Pairwise Ranking的损失函数。此外,双线性结构中,通过增加负样本的比例,不仅能大大减缓推荐系统的冷启动问题,而且还能提升模型的预测效果。在2个真实数据集上的实验结果表明,所提出的模型获得了更高的推荐准确率,在HR和NDCG等客观指标上都优于其他对比模型。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 real-world datasets show that the proposed model achieves higher recommendation accuracy and outperforms other models in objective metrics such as HR and NDCG.

关 键 词：隐式反馈 成对学习 因子分解机 神经网络 冷启动 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]