1 INTRODUCTION

2 OVERVIEW OF RECOMMENDER SYSTEMS AND DEEP LEARNING  
2.1 Recommender Systems  
2.2 Deep Learning Techniques  
2.3 Why Deep Neural Networks for Recommendation?  
2.4 On Potential Limitations  

3 DEEP LEARNING BASED RECOMMENDATION: STATE-OF-THE-ART  
3.1 Categories of Deep Learning Based Recommendation Models  
3.2 Multilayer Perceptron Based Recommendation  
3.3 Autoencoder Based Recommendation  
3.4 Convolutional Neural Networks Based Recommendation  
3.5 Recurrent Neural Networks Based Recommendation  
3.6 Restricted Boltzmann Machine Based Recommendation  
3.7 Neural Attention Based Recommendation  
3.8 Neural Auto Regressive Based Recommendation  
3.9 Deep Reinforcement Learning for Recommendation  
3.10 Adversarial Network Based Recommendation  
3.11 Deep Hybrid Models for Recommendation  

4 FUTURE RESEARCH DIRECTIONS AND OPEN ISSUES  
4.1 Joint Representation Learning from User and Item Content Information  
4.2 Explainable Recommendation with Deep Learning  
4.3 Going Deeper for Recommendation  
4.4 Machine Reasoning for Recommendation  
4.5 Cross Domain Recommendation with Deep Neural Networks  
4.6 Deep Multi-Task Learning for Recommendation  
4.7 Scalability of Deep Neural Networks for Recommendation  
4.8 The Field Needs Better, More Unified, and Harder Evaluation  

5 CONCLUSION