摘要： 为在信息推荐过程中挖掘出更多的信息关系,提出一种基于深度信念网络的信息推荐方法。利用模糊聚类进行预处理以达到跨类推荐的目的,根据用户浏览记录通过网络计算高分信息,并结合用户兴趣,使用潜在狄里克雷分配模型对高分信息进行权值调整,从而提高推荐准确率。在整个推荐过程中网络会根据用户行为对推荐信息权值进行相应调整。实验结果表明,该方法的推荐成功率比BP神经网络提高5.7%。

关键词: 信息推荐, 深度学习, 深度信念网络, 潜在狄利克雷分配模型, 模糊聚类

Abstract: In order to mine more information relationship in information recommendation,this paper puts forward an information recommendation method based on the Deep Belief Network(DBN).It uses fuzzy clustering to do some pretreatment in order to achieve the aim of cross-category recommentation.According to the user browsing history,it works out high-mark information through the Internet,and then uses Latent Dirichlet Allocation(LDA) model combining the interests of users to do some weight value adjustments.So as to improve the accuracy of recommendation.In the process of recommendation,the Internet will adjust the weight value of recommended information according to the action of users.Experimental results show that the recommendation accuracy of this method can be improved by 5.7% than BP neural network.

Key words: information recommendation, deep learning, Deep Belief Network(DBN), Latent Dirichlet Allocation(LDA) model, fuzzy clustering

中图分类号: 

• TP391