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


  • Autoencoder based recommendation
  • Big Data
  • Big data
  • Boltzmann machines
  • CTR
  • Collaborative denoising autoendoder
  • Collaborative topic regression
  • Collaborative variational autoencoder
  • DL techniques
  • Data handling techniques
  • Deep generative models
  • Deep learning models
  • Hybrid recommendation systems
  • Information analysis and indexing
  • Information filtering
  • Information networks
  • Information retrieval techniques
  • Knowledge engineering techniques
  • LDA
  • Latent dirichlet allocation
  • Learning (artificial intelligence)
  • Neural collaborative filtering
  • Neural computing techniques
  • PMF
  • Probabilistic matrix factorization
  • Recommender systems
  • Recurrent neural nets
  • Recurrent recommender network
  • Restricted boltzmann machine based collaborative filtering
  • Search engines

ASJC Scopus subject areas

  • Computer Science(all)


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