What we have done?

• Towards an Optimal City-wide Deployment of Advertisement (for Influcence Maximization) and Deployment of Gas stations, electric charging stations and any Facilities (for Doverage Maximization).
• Orthogonal to different choices of Coverage Measurement and Influence Measurement.
• When the Influence Overlaps Matter (KDD’18, TKDD’20)
• When the Impression Counts Matter (KDD’19)
• Optimal Site Selection for Retailer Store (VLDB’18)

What problems we are solving?

• Trajectory-driven Influential Billboard Placement: Given a set of billboards U (each with a location and a cost), a set T of user-movement records and a budget L, it aims to 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.
• When Impression Counts Meet Influential Billboard Placement: Given a set of billboards U (each with a location and a cost), a set T of user-movement records, an impression count threshold to trigger the influence of an individual user, a budget L, find a set of billboards within the budget to influence the largest number of trajectories. One core challenge is that different users can have different thresholds to be impressed by the same advertisement.

Demonstrations
Advertising Deployment
ITAA is nn intelligent trajectory-driven outdoor advertising deployment assistant ramework. It aims to find the optimal advertising deployment strategy, which supports different influence models, and achieves the different degrees of the effectiveness-efficiency trade-off.
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Advertising Deployment
What does a user expect when using a personalized map service for spatial object recommendation? We observe that the following questions may matter to cater for users’ intention: (1) what does the system recommend for users at different stages of data exploration when they zoom in or zoom out the map application? (2) why does the system recommend a particular spatial object to the users? In this project, we focus on the POI recommender system in a multi-level manner by involving spatial granularity information. To this end, we expect that users can be recommended effective POIs when they have personal requirements on spatial granularity, or even they do not provide any specific intentions on where to go. Our model can completely explore the intrinsic spatial containment relationship between a POI point and a suburb then provide recommendations for varying spatial granularity. Moreover, users will be acknowledged about additional hints on why such a recommendation is generated.