计算机工程2017，Vol.43Issue(12)：173-178,6.DOI:10.3969/j.issn.1000-3428.2017.12.032
基于降噪自编码器网络与词向量的信息推荐方法
Information Recommendation Method Based on Denoising Auto-encoder Network and Word Vector
摘要
Abstract
The recommendation method based on Denoising Auto-encoder(DAE) lacks of items co-occurrence analysis ability,and the model exists the problem of cold start of parameters.In order to solve these problems,this paper proposes an information recommendation method based on DAE network and word vector.A training corpus is built by mapping users into documents and mapping items into words.The word vector model is used to train the corpus to generate item vectors which contain implicit context information.All item vectors are used as the initial weights to reconstruct the DAE neural network,and the model parameters are obtained through training.The model is used to predict ratings to complete top-N recommendation.Experimental results on standard datasets show that the proposed method improves the accuracy of recommendation,and the training speed is better than that of DAE,Singular Value Decomposition (SVD) and Collaborative Filtering(CF) methods.关键词
信息推荐/神经网络/降噪自编码器/词向量/参数冷启动Key words
information recommendation/neural network/Denoising Auto-encoder (DAE)/word vector/cold start of parameter分类
信息技术与安全科学引用本文复制引用
郭喻栋,郭志刚,席耀一..基于降噪自编码器网络与词向量的信息推荐方法[J].计算机工程,2017,43(12):173-178,6.基金项目
国家社会科学基金“网上舆论斗争系统建模与应对策略研究”(14BXW028). （14BXW028）
