Variational Deep Collaborative Matrix Factorization for Social Recommendation
In this paper,we propose a Variational Deep Collaborative Matrix Factorization(VDCMF)algorithm for social recommendation that infers latent factors more effectively than existing methods by incorporating users social trust information and items content information into a unified generative framework.Unlike neural network-based algorithms,our model is not only effective in capturing the non-linearity among correlated variables but also powerful in predicting missing values under the robust collaborative inference.Specifically,we use variational auto-encoder to extract the latent representations of content and then incorporate them into traditional social trust factorization.We propose an efficient expectation-maximization inference algorithm to learn the models parameters and approximate the posteriors of latent factors.Experiments on two sparse datasets show that our VDCMF significantly outperforms major state-of-the-art CF methods for recommendation accuracy on common metrics.
Recommender System Matrix Factorization Deep Learning Generative model
Teng Xiao Hui Tian Hong Shen
School of Data and Computer Science,Sun Yat-sen University,Guangzhou,China School of Information and Communication Technology,Griffith University,Gold Coast,Australia School of Data and Computer Science,Sun Yat-sen University,Guangzhou,China;School of Computer Scienc
国际会议
澳门
英文
426-437
2019-04-14（万方平台首次上网日期，不代表论文的发表时间）