  Geo-social networks offer opportunities for the marketing and promotion of
geo-located services. In this setting, we explore a new problem, called
Maximizing the Influence of Bichromatic Reverse k Nearest Neighbors
(MaxInfBRkNN). The objective is to find a set of points of interest (POIs),
which are geo-textually and socially attractive to social influencers who are
expected to largely promote the POIs through online influence propagation. In
other words, the problem aims to detect an optimal set of POIs with the largest
word-of-mouth (WOM) marketing potential. This functionality is useful in
various real-life applications, including social advertising, location-based
viral marketing, and personalized POI recommendation. However, solving
MaxInfBRkNN with theoretical guarantees is challenging, because of the
prohibitive overheads on BRkNN retrieval in geo-social networks, and the NP and
#P-hardness in finding the optimal POI set. To achieve practical solutions, we
present a framework with carefully designed indexes, efficient batch BRkNN
processing algorithms, and alternative POI selection policies that support both
approximate and heuristic solutions. Extensive experiments on real and
synthetic datasets demonstrate the good performance of our proposed methods.
