Deep generative models for recommender systems

Vineeth Rakesh, Suhang Wang, Huan Liu

Research output: Chapter in Book/Report/Conference proceedingChapter


This chapter introduces some recent trends in generative and deep-learning (DL) models for hybrid recommendation systems that have proven to be extremely effective in integrating different modalities of data. It is organized into three main sections. The first section considers classic algorithms such as probabilistic matrix factorization and latent Dirichlet allocation and illustrates the generative principle of a hybrid recommendation model called collaborative topic regression that jointly models the latent interests of users and items. The second section presents recommendation models that are exclusively based on DL techniques. This includes models such as Restricted Boltzmann-machine-based CF, autoencoder (AE)-based recommendation, neural CF and recurrent recommender network. Finally, the third section explains models such as collaborative denoising AE and collaborative variational AE that integrates PGMs with DL to create a generative DL framework.

Original languageEnglish (US)
Title of host publicationBig Data Recommender Systems
Subtitle of host publicationAlgorithms, Architectures, Big Data, Security and Trust
PublisherInstitution of Engineering and Technology
Number of pages22
ISBN (Electronic)9781785619755
StatePublished - Jan 1 2019

All Science Journal Classification (ASJC) codes

  • General Computer Science


Dive into the research topics of 'Deep generative models for recommender systems'. Together they form a unique fingerprint.

Cite this