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We investigate how to leverage limited local information for mobile advertisement popularization, where users are motivated to propagate advertisements with rewards or credits distributed from a centralized platform. Previous solutions on social networks failed to be applicable for mobile advertisement propagation because of the mobility, which would lead to extremely high overhead and low propagation efficiency. Participants need to be selected carefully and efficiently in dealing with the highly dynamic network and uncertain contacts among users. Even worse, the propagation effects are difficult to quantify with the increased number of mobile users. In tackling these difficulties, we propose &#x03B1;MAP (&#x03B1; here means efficient mobile advertisement popularization with local information), a lightweight but effective propagation user selection scheme with local information. Two key technologies inspire us to achieve efficient and effective mobile advertisement propagation. First, we advocate propagation effects instead of influence for user selection, where mobile users with strong information dissemination ability could be selected. Second, we use local information instead of the global information to achieve near optimal performance for propagation. In our proposed scheme, the information potentials proposed by Loukas et al. are leveraged to find the influential users with local information. With extensive experimental study, we find that &#x03B1;MAP could effectively improve the mobile advertisements delivery ratio. Using the propagation instead of popularization is validated with different aspects of investigations when the budget is constrained. To evaluate the impact of mobility, we leverage the mobile trace data set for comprehensive evaluations. &#x03B1;MAP performs fairly well when more realistic experimental settings are incorporated.

Turning Small to Big: Efficient Mobile Advertisement Propagation With Local Information