Neural variational matrix factorization with side information for collaborative filtering

Teng Xiao, Hong Shen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings
EditorsSheng-Jun Huang, Min-Ling Zhang, Zhiguo Gong, Zhi-Hua Zhou, Qiang Yang
PublisherSpringer Verlag
Pages414-425
Number of pages12
ISBN (Print)9783030161477
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019 - Macau, China
Duration: 14 Apr 201917 Apr 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11439 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019
Country/TerritoryChina
CityMacau
Period14/04/1917/04/19

Keywords

  • Collaborative filtering
  • Deep generative process
  • Matrix factorization
  • Neural network
  • Variational inference

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