Neural Variational Matrix Factorization with Side Information for Collaborative Filtering
Probabilistic Matrix Factorization(PMF)is a popular technique for collaborative filtering(CF)in recommendation systems.The purpose of PMF is to find the latent factors for users and items by decomposing a user-item rating matrix.Most methods based on PMF suffer from data sparsity and result in poor latent representations of users and items.To alleviate this problem,we propose the neural variational matrix factorization(NVMF)model,a novel deep generative model that incorporates side information(features)of both users and items,to capture better latent representations of users and items for the task of CF recommendation.Our NVMF consists of two end-to-end variational autoencoder neural networks,namely user neural network and item neural network respectively,which are capable of learning complex nonlinear distributed representations of users and items through our proposed variational inference.We derive a Stochastic Gradient Variational Bayes(SGVB)algorithm to approximate the intractable posterior distributions.Experiments conducted on three publicly available datasets show that our NVMF significantly outperforms the state-of-the-art methods.
Collaborative filtering Neural network Matrix factorization Deep generative process Variational inference
Teng Xiao Hong Shen
School of Data and Computer Science,Sun Yat-sen University,Guangzhou,China School of Data and Computer Science,Sun Yat-sen University,Guangzhou,China;School of Computer Scienc
国际会议
澳门
英文
414-425
2019-04-14（万方平台首次上网日期，不代表论文的发表时间）