Variational deep collaborative matrix factorization for social recommendation

Teng Xiao, Hui Tian, Hong Shen

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

5 Citations (Scopus)


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 model’s 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.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings
EditorsQiang Yang, Zhiguo Gong, Zhi-Hua Zhou, Sheng-Jun Huang, Min-Ling Zhang
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783030161477
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


Conference23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019


  • Deep Learning
  • Generative model
  • Matrix Factorization
  • Recommender System


Dive into the research topics of 'Variational deep collaborative matrix factorization for social recommendation'. Together they form a unique fingerprint.

Cite this