重庆邮电大学学报（自然科学版）2024，Vol.36Issue(2)：383-392,10.DOI:10.3979/j.issn.1673-825X.202303020056
基于域矩阵因子分解机的点击通过率预估增强网络
Enhanced network for CTR prediction based on field-matrixed factorization machines
摘要
Abstract
Effective feature interaction plays a vital role in the accuracy of click-through-rate(CTR)estimation in industri-al recommendation systems.Previous CTR prediction models with a parallel structure learn low-order and high-order interac-tions of features by connecting independent shallow models and deep models in parallel.However,these models have prob-lems such as low accuracy of shallow models,failure to consider the multi-semantic problem of feature interaction,exces-sive parameters,and over-generalization of deep models.Based on the above problems,this paper proposes an enhanced network for CTR prediction based on field-matrixed factorization machines.It introduces domain matrix to optimize the inter-action in shallow models,improves the efficiency of computation,and adds a bridge module between the DNN layers of deep models to enhance the memory ability of original features after each high-order interaction.The results of shallow and deep models are added and normalized to obtain the predicted value.The model has undergone extensive experiments on Criteo,KKBox,Frappe,and MovieLens datasets,demonstrating excellent predictive capabilities.关键词
点击通过率/域矩阵因子分解机/桥接模块/特征交互Key words
click-through rate/field-matrixed factorization machine/bridging module/feature interaction分类
信息技术与安全科学引用本文复制引用
陈乔松,黄泽锰,胡静,王进,邓欣..基于域矩阵因子分解机的点击通过率预估增强网络[J].重庆邮电大学学报（自然科学版）,2024,36(2):383-392,10.基金项目
国家重点研发项目(2022YFE0101000) The National Key Research and Development Program of China(2022YFE0101000) （2022YFE0101000）
