Deep Meta-learning in Recommendation Systems: A Survey

By Chunyang Wang et al

Published on Aug. 10, 2018

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Table of Contents

1 INTRODUCTION
2 FOUNDATIONS
2.1 Meta-learning
2.1.1 Formalizing Meta-learning
2.1.2 Mainstream Frameworks of Meta-learning Techniques
3 TAXONOMY
4 META-LEARNING RECOMMENDATION TASK CONSTRUCTION
5 APPLYING META-LEARNING TECHNIQUES
6 FUTURE RESEARCH DIRECTIONS
7 CONCLUSION

Summary

This paper provides a comprehensive overview of deep meta-learning based recommendation systems. It discusses the challenges faced by deep learning-based recommendation methods due to data sparsity and computational inefficiency. The paper introduces meta-learning as a paradigm to improve learning efficiency and generalization ability. Various meta-learning techniques are applied to enhance deep recommendation models, especially in scenarios with limited data. The survey categorizes existing methods based on recommendation scenarios, meta-learning techniques, and meta-knowledge representations. It also highlights limitations in current research and suggests future research directions in this area.