Maximizing the influence of bichromatic reverse k nearest neighbors in geo-social networks

Geo-social networks offer opportunities for the marketing and promotion of geo-located services. In this setting, we explore a new problem, called Max imizing the Inf luence of B ichromatic R everse k N earest N eighbors (MaxInfBR k NN). The objective is to find a set of points of interest (POIs), which are geo-textually and socially relevant to social influencers who are expected to largely promote the POIs online. 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 MaxInfBR k NN with theoretical guarantees is challenging because of the prohibitive overheads on BR k NN retrieval in geo-social networks, and the NP and #P-hardness of finding the optimal POI set. To achieve practical solutions, we present a framework with carefully designed indexes, efficient batch BR k NN 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.