Xing Xie

Microsoft Research Asia

Title of Keynote: Knowledge Enhanced Recommendation Systems
Abstract: The explosive growth of online contents and services has provided overwhelming choices for users. Recommender Systems intend to address the information explosion by finding a small set of items for users to meet their personalized interests. In most recommendation scenarios, items may contain rich knowledge information. The network structure that captures such knowledge is referred to as the knowledge graph. The knowledge graph greatly expands the amount of information of each item and strengthens the connection between them, providing abundant reference values for a recommendation engine, which leads to additional diversity and explainability of the recommendation result. In this talk, I will introduce opportunities and challenges, as well as our recent research works in this area.
Biography: Xing Xie is currently a senior principal research manager at Microsoft Research Asia, and a guest Ph.D. advisor at the University of Science and Technology of China. He received his B.S. and Ph.D. degrees in Computer Science from the University of Science and Technology of China in 1996 and 2001, respectively. He joined Microsoft Research Asia in July 2001, working on data mining, social computing and ubiquitous computing. During the past years, he has published over 300 referred journal and conference papers, won the best student paper award in KDD 2016, and the best paper awards in ICDM 2013 and UIC 2010. He has more than 50 patents filed or granted. He has been invited to give keynote speeches at MDM 2019, HHME 2018, ASONAM 2017, MobiQuitous 2016, SocInfo 2015, W2GIS 2011, etc. He currently serves on the editorial boards of ACM Transactions on Social Computing (TSC), ACM Transactions on Intelligent Systems and Technology (TIST), Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), Springer GeoInformatica, Elsevier Pervasive and Mobile Computing, CCF Transactions on Pervasive Computing and Interaction (CCF TPCI). In recent years, he was involved in the program or organizing committees of over 70 conferences and workshops. Especially, he served as program co-chair of ACM Ubicomp 2011, the 8th Chinese Pervasive Computing Conference (PCC 2012), the 12th IEEE International Conference on Ubiquitous Intelligence and Computing (UIC 2015), and the 6th National Conference on Social Media Processing (SMP 2017), and will be a program co-chair of ACM SIGSPATIAL 2020. In Oct. 2009, he founded the SIGSPATIAL China chapter which was the first regional chapter of ACM SIGSPATIAL. He is a senior member of ACM and the IEEE, and a distinguished member of China Computer Federation (CCF).

Shirui Pan

Monash University

Title of Keynote: Towards Dynamic and Efficient Graph Neural Networks
Abstract: Recent years have witnessed a wide range of applications (e.g., social networks, citation networks, and biological networks) where the data exhibits complex relation and inter-dependency among objects. Learning from graph data has imposed significant challenges to traditional machine learning algorithms, which mostly require that the data are in forms of vector format. Graph neural networks have recently emerged as a new learning paradigm to many graph analytics tasks such as classification, clustering, and link prediction. In this talk, I will briefly connect the graph neural networks (GNNs) to existing RNN or CNN architectures. As GNNs mostly focus on static graphs, I will talk about our recent study on dynamic GNN which models spatial-temporal graphs. This model can be used for traffic speed prediction, human activity recognition and taxi flow prediction. Inspired by GNNs, I will next introduce how to learn discrete network embedding to reduce storage cost and increase computational efficiency. Future directions of GNNs will be also highlighted.
Biography: Shirui Pan is a Lecturer (a.k.a. Assistant Professor) with the Machine Learning Group, Faculty of Information Technology, Monash University. Prior to this, he was a Lecturer with the Centre for Artificial Intelligence (CAI), School of Software, University of Technology Sydney(UTS). Shirui received his Ph.D degree in computer science from UTS, Australia. To date, Dr Pan has published over 80 research papers in top-tier journals and conferences, including the IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Transactions on Cybernetics (TCYB), ICDE, AAAI, IJCAI, ICDM, SDM, PAKDD. He is a program committee member and an invited reviewer for many top conferences and journals including NeurIPS, KDD, ICML, TPAMI, and TKDE. Dr Pan's research interests include data mining and machine learning, specialized in graph mining and network analysis.