报告人： Will Wei Sun, Department of Management Science, University of Miami
时间：2017-06-13 14:00 ~ 15:00
地点：1114M, Science Building 1 (理科1号楼)
Tensor as a multi-dimensional generalization of matrix has received increasing attention in industry due to its success in personalized recommendation systems. Traditional recommendation systems are mainly based on the user-item matrix, whose entry denotes each user’s preference for a particular item. To incorporate additional information into the analysis, such as the temporal behavior of users, we encounter a user-item-time tensor. Existing tensor decomposition methods for personalized recommendation are mostly established in the non-sparse regime where the decomposition components include all features. For high dimensional tensor-valued data, many features in the components essentially contain no information about the tensor structure, and thus there is a great need for a more appropriate method that can simultaneously perform tensor decomposition and select informative features. In this talk, I will discuss a new sparse tensor decomposition method that incorporates the sparsity of each decomposition component to the CP tensor decomposition. Specifically, the sparsity is achieved via an efficient truncation procedure to directly solve an L0 sparsity constraint. In theory, in spite of the non-convexity of the optimization problem, it is proven that an alternating updating algorithm attains an estimator whose rate of convergence significantly improves those shown in non-sparse decomposition methods. As a by-product, our method is also applicable to conduct dynamic clustering of tensor-variate samples. I will show the advantages of our method in two real applications, click-through rate prediction for online advertising and dynamic clustering for brain imaging data.
About the Speaker:
Will Wei Sun is currently an assistant professor at department of Management Science, University of Miami, Florida. Before that, he was a research scientist in the advertising science team at Yahoo labs. He obtained his PhD in Statistics from Purdue University in 2015. Dr. Sun’s research focuses on machine learning and big data theory, with applications in computational advertising, personalized recommendation system, and Neuroimaging analysis.