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