Deep Learning Based Recommender Systems: Latest Advances and Prospects

Dear Colleagues,

Deep learning is a subset of machine learning that mimics the functionalities of the human brain to compute correlations and find patterns by processing data with a specified logical structure. It is among the leading research topics that attracts the attention of researchers from both industry and academia. It involves learning through layers that allow computers to develop a hierarchy of complicated concepts from simpler ones.

Recently, deep learning technology has achieved great success in the areas of speech recognition, computer vision, and natural language processing, and recommendation systems (RSs) can benefit from these breakthroughs. Recommender systems are intelligent decision support software tools that help users to discover items that might be of interest to them. They are capable of mitigating the problem of information overload.

Today, deep-learning-based recommendation algorithms have seen remarkable progress in aspects such as powerful representation learning capability, deep collaborative filtering, and deep interaction between features. Deep learning can be further applied to a great number of potential recommendation scenarios, such as efficiency and scalability of large-scale (industrial level) recommender systems, the capture of users’ long- and short-term preferences, diversity data fusion, and so on.

This Special Issue invites submissions (surveys, reviews, and latest advances) on all topics of deep learning for recommender systems, including but not limited to:

• Deep, universal, and dynamic user profiling from multi-source heterogeneous data in RSs;
• Efficiency and scalability of large-scale deep learning-based recommender systems;
• Collaborative filtering recommender systems based on deep learning;
• Content-based filtering recommender systems based on deep learning;
• Deep learning for multicriteria recommender systems;
• Deep learning for context-aware recommender systems;
• Deep-learning-based e-health recommender systems;
• Applications of deep-learning-based RSs in different domains, such as social networks, intelligent traffic systems, e-services, e-government, e-learning, e-commerce, programming education, educational technologies, etc.;
• Other topics related to RSs and AI such as robotics, intelligent software systems, etc.

Dr. Mohamed Hamada
Dr. Yutaka Watanobe
Guest Editors

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