TY - GEN
T1 - Neural variational matrix factorization with side information for collaborative filtering
AU - Xiao, Teng
AU - Shen, Hong
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Collaborative filtering
KW - Deep generative process
KW - Matrix factorization
KW - Neural network
KW - Variational inference
UR - http://www.scopus.com/inward/record.url?scp=85064890394&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-16148-4_32
DO - 10.1007/978-3-030-16148-4_32
M3 - Conference contribution
AN - SCOPUS:85064890394
SN - 9783030161477
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 414
EP - 425
BT - Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings
A2 - Huang, Sheng-Jun
A2 - Zhang, Min-Ling
A2 - Gong, Zhiguo
A2 - Zhou, Zhi-Hua
A2 - Yang, Qiang
PB - Springer Verlag
T2 - 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019
Y2 - 14 April 2019 through 17 April 2019
ER -