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## Deep generative models for recommender systems

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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.

Chapter Contents:

• 6.1 Introduction
• 6.2 Generative models
• 6.2.1 Probabilistic matrix factorization
• 6.2.2 Probabilistic latent semantic analysis
• 6.2.3 Latent Dirichlet allocation
• 6.2.4 Collaborative topic models
• 6.3 Deep learning for recommender systems
• 6.3.1 Restricted Boltzmann-machine-based collaborative filtering
• 6.3.2 Autoencoder for recommender systems
• 6.3.3 Multilayer perceptron based recommender systems
• 6.3.4 RNN/LSTM for recommendation
• 6.4 Deep generative models
• 6.4.1 Collaborative denoising autoencoders
• 6.4.2 Collaborative variational autoencoder
• 6.5 Summary
• References

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