基于深度受限玻尔兹曼机的个性化推荐算法

Personalized Recommendation Algorithm Based on Depth-Restricted Boltzmann Machine

为了提高个性化推荐系统性能,提出了一种基于深度受限玻尔兹曼机的个性化推荐方法。首先通过提取推荐系统的用户和资源特征构建多层受限玻尔兹曼机(Restricted Boltzmann machine,RBM)网络,从而形成深度受限玻尔兹曼机个性化推荐模型;其次通过可视和隐藏层的边缘概率分布求解待推荐训练样本的最大似然度;然后结合对比散度(Contrast divergence,CD)重构来获得RBM主要参数更新方式,并通过可视和隐藏层的正反向更新,来获得稳定的RBM结构;最后利用计算用户资源评分值实现个性化推荐。实验结果表明,在训练样本稀疏度合理范围内,与常用个性化推荐算法比较,所提方法通过合理控制RBM深度和设置合适的隐藏层节点数,能够获得更优的均方根误差(Root mean squared error,RMSE)性能。

To improve the performance of personalized recommendation system,a personalized recommendation method based on depth-restricted Boltzmann machine is proposed.Firstly,by extracting the characteristics of users and resources of the recommendation system,a multi-layer restricted Boltzmann machine(RBM)network is constructed,thus forming a personalized recommendation model of depthrestricted Boltzmann machine.Secondly,the maximum likelihood of the training samples to be recommended is calculated by the marginal probability distribution of visible and hidden layers.Then,combined with contrastive divergence(CD)reconstruction,the main parameter updating mode of RBM is obtained,and the stable RBM structure is obtained by updating the visible and hidden layers in both directions.Finally,the personalized recommendation is realized by calculating the user resource score.Experimental results show that,within the reasonable range of sparsity of training samples,compared with the commonly used personalized recommendation algorithms,the proposed method can obtain better root mean squared error(RMSE)performance by reasonably controlling the depth of RBM and setting the appropriate number of hidden layer nodes.