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Leveraging the Power of Social Propagations:Algorithm Designs for Social Marketing
Leveraging the Power of Social Propagations:Algorithm Designs for Social Marketing
教师介绍

本讲教师:Yu Yang
所属学科:经管
人  气:1268

课程介绍
报告简介:Social propagation is a fundamental and prevalent process taking place in social networks, where dueto the peer influence of social network users, behaviors of a few influential users can spread widely ina network. Leveraging the social propagation effect lies at the core of mining marketing values ofsocial networks, and has been studied extensively in the past decade. Most studies aimed atidentifying influential users in a given social network, the first step of making good use of propagationin social marketing. However, in many deeper marketing applications, such as the real-timerecommendation of influential users and personalized pricing in promotional campaign planning,effectively mining social propagations faces many unsettled and challenging computational problems.In this talk, I will introduce some of our recent progress in algorithmic methods for exploiting socialpropagations in marketing. We investigated three crucial problems along this line: (1) how to efficientlymonitor top influential users in a fast-evolving social network; (2) how to cost-effectively motivateinfluential users to trigger a large-scale propagation for our marketing purposes; (3) how to maximizesocial network users' interaction/ discussion on a topic by scheduling an effective informationdiffusion. Our work provides powerful algorithmic tools to solve these problems effectively, which atthe same time are efficient and can deal with large networks containing millions or even tens ofmillions of vertices in a single machine. I will conclude by discussing some future directions in miningsocial propagations. 报告人简介:Yu Yang is currently an Assistant Professor with the School of Data Science at the City University ofHong Kong. His research interests lie in the algorithmic aspects of data mining and data science, withan emphasis on managing and mining dynamics of large-scale networks, His work appears in premiervenues such as SIGMOD, VLDB, ICDE, IJCAI, TKDE, TKDD, and KAIS. He obtained his Ph.D. inComputing Science from the Simon Fraser University in Feb. 2019. Before that, he received his B.E.degree from the Hefei University of Technology in 2010, and his M.E. degree from the University ofScience and Technology of China in 2013, both in Computer Science.

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