Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  target the location promotion problem in location-based social networks (LBSNs). The location promotion problem is given a location,  select a set of users as seeds to influence as many users as possible who are likely to visit a selected location. Specifically,  model the location promotion problem as an influence maximization problem on a graph and explore the independent cascading diffusion model on the graph. To determine the propagation probability of the edges of our proposed graph, the relation beten users and the selected location should be detected. A property of LBSN is that the major reason of users visiting a location is based on their mobility. Therefore,  propose a mobility model DMM (Distance-based Mobility Model) to model each user's mobility. DMM exploits random walk with restart and the por law property of users' movements. Based on DMM and the selected location, the propagation probability of edges can be derived. In the evaluation,  show the performance of our proposed algorithms on two real datasets.
['Wen Yuan Zhu', 'Wen Chih Peng', 'Ling Jyh Chen']
I am writing a research paper on a idea Exploiting mobility for location promotion in location-based social networks
[{"from": "human", "value": "I am writing a research paper on a idea Exploiting mobility for location promotion in location-based social networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  target the location promotion problem in location-based social networks (LBSNs). The location promotion problem is given a location,  select a set of users as seeds to influence as many users as possible who are likely to visit a selected location. Specifically,  model the location promotion problem as an influence maximization problem on a graph and explore the independent cascading diffusion model on the graph. To determine the propagation probability of the edges of our proposed graph, the relation beten users and the selected location should be detected. A property of LBSN is that the major reason of users visiting a location is based on their mobility. Therefore,  propose a mobility model DMM (Distance-based Mobility Model) to model each user's mobility. DMM exploits random walk with restart and the por law property of users' movements. Based on DMM and the selected location, the propagation probability of edges can be derived. In the evaluation,  show the performance of our proposed algorithms on two real datasets."}]