Research Topic

Deep Learning for Recommender Systems

About this Research Topic

Nowadays, the renaissance of artificial intelligence (AI) has attracted huge attention from every corner of the world. On the one hand, deep learning algorithms and theories have nearly dominated AI development in almost all areas, e.g., natural language processing (NLP), computer vision (CV) and planning and ...

Nowadays, the renaissance of artificial intelligence (AI) has attracted huge attention from every corner of the world. On the one hand, deep learning algorithms and theories have nearly dominated AI development in almost all areas, e.g., natural language processing (NLP), computer vision (CV) and planning and have shown great promise. On the other hand, recommender systems (RS), as one of the most popular and important applications of AI, are being widely planted into our daily lives and have made a huge difference. Naturally, the combination of deep learning and recommender systems has been flourishing for years and has shown great potential. In practice, deep learning has nearly dominated the recommender system research in recent years. Many state-of-the-art recommender systems are built on deep learning models.

This Research Topic solicits the latest and significant contributions on developing and applying deep learning algorithms and theories for building intelligent recommender systems, including cutting-edge theories, foundations, and learning systems. As well as actionable tools and impactful case studies of deep learning based recommender systems. Those theories and algorithms that focus on the particular issues in recommender systems, including interaction, preference elicitation, privacy, trust, accountability, emotions/personality etc. are most welcome. Also, works that focus on emerging application domains of recommender systems, including service, health care, education, finance, entertainment, etc. are particularly welcome. All the submissions will go into a quick and high-quality peer-review process for fast publication.

The Research Topic invites submissions on all topics of theories, algorithms and applications for deep learning based recommender systems, including but not limited to:

-Deep neural model for recommender systems
-Shallow neural model for recommender systems
-Neural theories particularly for recommender systems
-Theoretical analysis of neural models for recommender systems
-Theoretical analysis for recommender systems
-Data characteristics and complexity analysis in recommender systems
-Non-IID (non-independent and identical distribution) theories and practices for recommender systems
-Auto ML for recommender systems
-Privacy issues in recommender systems
-Recommendations on small data sets
-Complex behavior modeling and analysis for recommender systems
-Psychology-driven user modeling for recommender systems
-Brain-inspired neural models for recommender systems
-Explainable recommender systems
-Adversarial recommender systems
-Multimodal recommender systems
-Rich-context recommender systems
-Heterogeneous relation modeling in recommender systems
-Visualization in recommender systems
-New evaluation metrics and methods for recommender systems
-Applications or case studies of recommender systems


Keywords: Recommender Systems, Recommendations, Deep Learning, Behaviour Modelling, Recommendation Systems


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

10 April 2021 Manuscript
19 November 2021 Manuscript Extension

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

10 April 2021 Manuscript
19 November 2021 Manuscript Extension

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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