• 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)
Questions?
Contact the Guest Editors at is5-20@computer.org
