计算机与现代化Issue(6)：1-7,114,8.DOI:10.3969/j.issn.1006-2475.2024.06.001
基于DNN与注意力机制的推荐算法模型
Recommendation Algorithm Model Based on DNN and Attention Mechanism
摘要
Abstract
In order to solve the defect of factorization machine in extracting high-order combination features and learn more use-ful feature information better,this paper attempts to use factorization machine to extract cross-feature and learn key feature infor-mation from low and high-order combination features by combining attention network,deep neural network,multi-head self-attention mechanism and other methods.Finally,the weighted fusion results were obtained according to the importance of the combination features of different orders,and the click-through rate of advertisements was estimated.The experiment was mainly carried out based on the advertising data set Criteo,and the analogy experiment was carried out with MovieLens data set to verify the effectiveness of the proposed algorithm model.The experimental results showed that compared with the benchmark model,in the two data sets,the AUC index increased by 2.32 percntage points and 0.4 percntage points.关键词
因子分解机/神经网络/注意力网络/特征提取Key words
factorization machine/neural network/attention network/extract cross-feature分类
信息技术与安全科学引用本文复制引用
周超,丛鑫,訾玲玲,肖谷平..基于DNN与注意力机制的推荐算法模型[J].计算机与现代化,2024,(6):1-7,114,8.基金项目
重庆师范大学博士启动基金/人才引进项目(21XLB030,21XLB029) （21XLB030,21XLB029）
重庆市教育科学"十四五"规划重点课题(K22YE205098) （K22YE205098）
