Social Media Retrieval and Recommendation
The rapid growth in popularity of social networking services (SNSs) is fundamentally reshaping the way we communicate. These SNSs, combined with portable and economic multimedia devices, have formed a new Internet era, where multimedia content creation and sharing are daily practice of the masses. As time goes by, large quantities of user-generated social media data have been archived, which greatly enrich the whole Internet environment. At the same time, this development has opened new challenges: the problem of information overload has become more and more serious. It gets harder for the information seekers to find their desired information, much like finding a needle in a haystack! Indeed, considerable efforts from different research communities have already been dedicated worldwide to the techniques of multimedia retrieval and recommendation, which are essential in order to provide information relevant to users’ specific information needs. Besides, many commercial systems, such as Pinterest and Vine, have successfully implemented personalized search and recommendation services in a convenient and flexible way. However, the unbounded growth of social media and user-generated content pushes the multimedia retrieval and recommendation technologies to their limits and thus demands new solutions.
This special session aims to provide a central forum for researchers with different backgrounds and from the diverse fields of engineering and research to present their contributions on solutions, models and theories that tackle the key issues in searching, recommending and discovering multimedia content, as well as a variety of multimedia applications based on search and recommendation technologies.
Topics of Interest
Topics of interest include, but are not limited to:
- Social media and cross-media retrieval and recommendation
- Domain-specific social media analysis
- Product search and recommendation
- Interactive and collaborative social media search
- Data representation and deep learning for social media
- Information extraction and visualization from social media
- Novel data sets and surveys of social media
- Large-scale social media indexing, ranking, and re-ranking
Liqiang Nie, National University of Singapore, Singapore
Dr. Liqiang Nie is currently a research fellow in the School of Computing, National University of Singapore. He respectively received his B.E. degree from Xi’an Jiaotong University of China, Xi’an, in 2009, and the Ph.D. degree from National University of Singapore, in 2013. His research interests include media search and healthcare analytics. Various parts of his work have been published in first-tier venues including SIGIR, MM, IJCAI, TOIS and TKDE. Dr. Nie has served as guest editors of some journals and conferences, such as IEEE TBD, MTAP, MMM and ICMR, and reviewers for a rich set of forums, e.g., SIGIR, MM, TKDE, TOIS, and KDD.
Yan Yan, University of Trento, Italy
Dr. Yan Yan received the PhD degree in computer science from the University of Trento, Italy, in 2014. He is currently a research fellow with the MHUG group at the University of Trento, Italy. He was a visiting scholar with Carnegie Mellon University in 2013 and a visiting research fellow with the Advanced Digital Sciences Center (ADSC), UIUC, Singapore in 2015. His research interests include computer vision, machine learning, and multimedia. Dr. Yan received the Best Student Paper Award in ICPR 2014 and Best paper Award in ACM Multimedia 2015. He has been PC members for several major conferences and reviewers for referred journals in computer vision and multimedia area. He has served as guest editors in IEEE Transactions on Pattern Analysis and Machine Intelligence. He is a member of the ACM and IEEE.
Benoit Huet, EURECOM, France
Dr. Benoit Huet (m) is Assistant Professor in the multimedia information processing group of Eurecom (France). He received his BSc degree in computer science and engineering from the École Supérieure de Technologie Électrique (Groupe ESIEE, France) in 1992. In 1993, he was awarded the MSc degree in Artificial Intelligence from the University of Westminster (UK) with distinction, where he then spent two years working as a research and teaching assistant. He received his DPhil degree in Computer Science from the University of York (UK) for his research on the topic of object recognition from large databases. He was awarded the HDR (Habilitation to Direct Research) from the University of Nice Sophia Antipolis, France, in October 2012 on the topic of Multimedia Content Understanding: Bringing Context to Content. He is associate editor for IEEE Multimedia, IEEE Transaction on Multimedia, Multimedia Tools and Application (Springer) and Multimedia Systems (Springer) and has been guest editor for a number of special issues (IEEE Multimedia, IEEE trans. on Multimedia, Springer Multimedia Tools and Application, EURASIP Journal on Image and Video Processing, , etc..). He regularly serves on the technical program committee of the top conferences of the field (ACM MM/ICMR, IEEE ICME/ICIP). He chairs the IEEE MMTC Interest Group on Visual Analysis, Interaction and Content Management (VAIG). He has co-authored over 150 papers in Books, Journals and International conferences. His current research interests include Large Scale Multimedia Content Analysis, Mining and Indexing – Multimodal Fusion – Socially-Aware Multimedia. He is a member of the ACM and IEEE.