CLOSED Call for Papers: Intelligent Recommendation with Advanced AI and Learning

• Paper submission due: CLOSED • First-round review due: 25 February 2020 • Revision due: 30 March 2020 • Final decision notification: 10 April 2020 • Camera-ready submission due: 30 April 2020 • Publication: Sept./Oct. 2020 Recommendation has become one of the most important applications of artificial intelligence (AI), data science, and advanced analytics theories and techniques. It is deeply integrated into our daily life. Data science, advanced learning, and AI techniques constitute the formal background employed to build advanced intelligent recommendations. This special issue aims to collect the state-of-the-art theories, tools, and applications for intelligent recommendation, enabled by advanced learning and AI techniques, data science, and advanced analytics.

Scope of Interest

Advanced AI and learning have been driving a variety of intelligent recommendation issues, including intent and preference modelling, non-IID recommendations, personalized recommendations, real-time recommendations, next-best recommendations, cross-domain recommendations, etc. in a context-aware, real-time, sequential, and user/product/domain-specific manner. This special issue aims to collect the most recent theoretical and practical advances in RS, including cutting-edge theories, foundations and learning systems as well as actionable tools and impactful case studies of intelligent recommendations, supported by advanced AI and machine learning techniques, in particular, deep learning, data science, and advanced analytics. Topics of interest include but are not restricted to:

Actionable and explainable recommendations
Context-aware recommendations
Cross-domain recommendations
Dynamic recommendations
Group recommendations
Intent and preference learning in recommendations
Large-scale recommendations
Multi-purpose recommendations
Next-best action recommendation
Non-IID (non-independent and identically distributed) recommendations
Online interactive recommendations
Personalized recommendations
Precision recommendations
Recommendations on massive sparse data
Real-time recommendations
Session-based recommendations
Sequential recommendations
Social recommendations
User modelling and profiling in recommendations
Impactful recommendation applications and systems

All submissions must comply with the IEEE Intelligent Systems’ submission guidelines and will be reviewed by research peers.

Guest Editors

Shoujin Wang, Macquarie University, Australia (shoujin.wang@mq.edu.au) Gabriella Pasi, University of Milano-Bicocca, Italy (pasi@disco.unimib.it) Liang Hu, University of Shanghai for Science and Technology, China (rainmilk@gmail.com) Longbing Cao, University of Technology Sydney, Australia (longbing.cao@uts.edu.au)