摘 要：

In this talk I will present our work as titled. In particular, we study two common scenarios. Goal 1 (our KDD18 work): Maximize the number of disintct users when influence overlap is considered - given a set of billboards U (each with a location and a cost), a database of trajectories T and a budget L, find a set of billboards within the budget to influence the largest number of trajectories. Goal 2 (our KDD19 work): Maximize the influence for outdoor advertising when impression counts are taken into consideration - In line with the advertising consumer behavior studies, we adopt the logistic function to take impression counts of a billboard to a user trajectory into consideration when defining the influence measurement.

报告人简介:

Zhifeng Bao is an associate professor in Computer Science, RMIT (Royal Melbourne Institute of Technology) University and an Honorary Fellow at the University of Melbourne, Australia. He received his PhD from the CS Dept at NUS in 2011. Zhifeng was the only recipient of the Best PhD Thesis Award in School of Computing and was the winner of the Singapore IDA (Infocomm Development Authority) gold medal. Zhifeng is a two-time winner of the Google Faculty Research Award 2015. His research interests include data visualization,  spatial data analytics and data integration. He serves the Associate Editor of VLDB Vol 14, and was the PC Co-chair of WSDM19 Cup, DASFAA17 (workshop track), ER18 (demo track), and the PC member of top conferences such as VLDB17-20, SIGMOD18, SIGIR15-19, ICDE16-20, WWW 18-19. Zhifeng has received five best paper awards such as KDD 2019 Best Paper Award Runnerup, DASFAA17 Best Student Paper Runnerup, and six best paper nominations such as KDD 2018, ICDE 2009, CIKM 2014. Since 2015 he has secured more than 1.5 million AUD funding as the chief investigator from Australasian Research Council, CSIRO, Google, etc.

时间：10月22日(星期二)14:00

地点：计算机科学技术楼230室