Latent factor model has a long history in statistics and can be dated back to early 1900s. In this talk, we will talk about a number of modern applications of latent factor model, including recommender system, network embedding, as well as crowdsourcing. Interestingly, these applications are often referred to as the unstructured data, but all lead to similar matrix type of data structure, and thus are particularly suitable for latent factor model. We will develop some exible yet powerful modeling strategies based on latent factor model, to tackle the cold-start issue in recommender system, community detection in networks, and label inference in crowdsourcing. The developed models are supported by a variety of real applications as well as their asymptotic properties.
Speaker: Professor Junhui WANG
Date: 6 November 2020 (Fri)
Time: 2:00pm – 3:00pm
Poster: Click here
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Biography
Prof Junhui WANG is Professor in the School of Data Science at City University of Hong Kong. He received his B.S. in Probability and Statistics from Peking University, and Ph.D. in Statistics from University of Minnesota. Before joining CityU, he was a faculty member at Columbia University and University of Illinois at Chicago. His research interests include statistical machine learning as well as its applications in biomedicine, economics, finance and information technology. He currently serves as Associate Editor of Statistica Sinica, Annals of the Institute of Statistical Mathematics, and Statistics and its interface.