Trajectory-driven Influential Billboard Placement
In this talk I will present our recent work accepted by KDD 2018. In this paper we propose and study the problem of trajectory-driven influential billboard placement: 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. One core challenge is to identify and reduce the overlap of the influence from different billboards to the same trajectories, while keeping the budget constraint into consideration. We show that this problem is NP-hard and present an enumeration based algorithm with (1 - 1/e) approximation ratio. However, the enumeration should be very costly when |U | is large. By exploiting the locality property of billboards’ influence, we propose a partition- based framework PartSel. PartSel partitions U into a set of small clusters, computes the locally influential billboards for each cluster, and merges them to generate the global solution. Since the local solutions can be obtained much more efficient than the global one, PartSel should reduce the computation cost greatly; meanwhile it achieves a non-trivial approximation ratio guarantee. Then we propose a LazyProbe method to further prune billboards with low marginal influence, while achieving the same approximation ratio as PartSel. Experiments on real datasets verify the efficiency and effectiveness of our methods.
Zhifeng Bao is an Associate Professor in Computer Science, RMIT (Royal Melbourne Institute of Technology) university, 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 was a winner of the Google Faculty Research Award 2015. His research interests include data visualization, spatial data analytics for smart transportation and tourism, machine learning and graph data management. He served the PC Co-chair of DASFAA17, ER18, APWEB16, WSDM19 Cup, etc, and served the PC member of top conferences such as VLDB17-18, SIGMOD18, SIGIR15-18, ICDE16-19, IJCAI16. Zhifeng has received four best paper awards such as DASFAA17, ADC16, and five best paper nomination such as IEEE ICDE 2009, CIKM 2014. Since 2015 he has secured more than 1 million AUD funding as the chief investigator from Australasian Research Council, CSIRO and Google.