With the rapid development of wireless communication technologies, location-based social networks, such as Foursquare and Gowalla, have become very popular. This makes it possible to mine user’s preference on locations and provided favourite recommendations. However, check-in data is sparse, long-tail, temporal and sociability. In this talk, we consider recommendation system using tensor method for handling such types of data with various techniques. Experiments on a real check-in database show that the proposed method can offer better location recommendation. The work is done jointly with Yiyuan LIU, and Ya WANG.


Biography: Bing-yi JING is a Chair Professor of the Department of Statistics and Data Science, Southern University of Science and Technology. He obtained a Ph.D. in Statistics from the University of Sydney in 1993; he worked as a postdoc at the Australian National University from 1992-1994; he was with the Hong Kong University of Science and Technology from 1994-2022. Jing Bingyi's research interests include Probability and Statistics, Financial Econometrics, Machine Learning, Bioinformatics, Network Data, Reinforcement Learning, etc. He has published more than 100 papers, many of which appeared in Ann. Stat., JASA, JRSSB, Biometrika, Ann. Probab., JoE, JBES, JMLR. He is a Fellow of the American Statistical Association (ASA) and the Institute of Mathematical Statistics (IMS), an elected member of the International Statistical Institute (ISI), and is serving/served as an Associate Editor of seven international journals, including Ann. Appl. Probab., Journal of Business & Economic Statistics, etc.


Bing-yi Jing

Research Area

Statistics seminar


Southern University of Science and Technology, China.


Friday, 28 July 2023, 11 am


Anita Lawrence Centre (H13) East Room 4082 and Zoom (link below)