现代电子技术2016，Vol.39Issue(23)：165-169,5.DOI:10.16652/j.issn.1004-373x.2016.23.041
基于嵌入式向量和循环神经网络的用户行为预测方法
User behavior prediction method based on embedded vector and recurrent neural network
摘要
Abstract
In order to further describe the effect of time factor on user behavior,and improve the recommendation effect of the recommendation system,a user behavior prediction method based on embedded vector and recurrent neural network is pro⁃posed by comprehensively considering the long⁃term and short⁃term user behavior features. According to all user behavior data in recommendation system,the users and commodities are embedded into the same feature space. The embedded vector is used to re⁃flect the long⁃term user features. According to the time series of each user′s historic behavior,the user behavior prediction model was built based on recurrent neural network to describe this user′s short⁃term behavior feature. The experiment results show that, the proposed method has better recommendation effect than that of the feature⁃level time series analysis method and other methods.关键词
循环神经网络/深度学习/嵌入式向量/用户行为预测/时间序列Key words
recurrent neural network/deep learning/embedded vector/user behavior prediction/time series分类
信息技术与安全科学引用本文复制引用
刘杨涛,南书坡,杨新锋..基于嵌入式向量和循环神经网络的用户行为预测方法[J].现代电子技术,2016,39(23):165-169,5.基金项目
河南省科技攻关重点计划项目（122102210563，132102210215）；河南省高等学校重点科研项目计划 （）
