论文标题
最大化双重反向K最近的邻居在地理社会网络中
Maximizing the Influence of Bichromatic Reverse k Nearest Neighbors in Geo-Social Networks
论文作者
论文摘要
地理社会网络为营销和推广地理位置服务提供了机会。在这种情况下，我们探索了一个新问题，称为最大化双重反向K最近的邻居（Maxinfbrknn）的影响。目的是找到一组兴趣点（POI），这些兴趣点对社会有影响力的人具有地缘文本和社会吸引力，这些社会影响者有望通过在线影响传播在很大程度上促进POI。换句话说，问题旨在检测具有最大口碑（WOM）营销潜力的最佳POI。此功能在各种现实生活中很有用，包括社交广告，基于位置的病毒营销和个性化POI建议。但是，由于地理社会网络中BRKNN检索的高尚开销以及在寻找最佳POI集时，用理论保证解决Maxinfbrknn是具有挑战性的。为了实现实用的解决方案，我们提出了一个框架，该框架具有精心设计的索引，有效的批处理BRKNN处理算法以及支持近似和启发式解决方案的替代POI选择策略。关于真实和合成数据集的广泛实验证明了我们提出的方法的良好性能。
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.
