Conference Proceedings

Trajectory-driven influential billboard placement

P Zhang, G Li, Z Bao, Y Zhang, Y Li, Z Peng

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining | Published : 2018

Abstract

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-based method is costly when |U | is large. By exploiting the locality proper..

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University of Melbourne Researchers

Grants

Awarded by Ministry of Science and Technology of China


Awarded by National Key Research & Development Program of China


Awarded by ARC


Awarded by NSFC


Awarded by 973 Program of China


Funding Acknowledgements

Zhiyong Peng was supported by the Ministry of Science and Technology of China (2016YFB1000700), and National Key Research & Development Program of China (2018YF-B1003400). Zhifeng Bao was supported by ARC (DP170102726, DP180102050), NSFC (61728204, 91646204), and was a recipient of Google Faculty Award. Guoliang Li was supported by the 973 Program of China (2015CB358700), NSFC (61632016, 61472198, 61521002, 61661166012) and TAL education.