Business location selection based on geo-social networks

© 2020, Springer Nature Switzerland AG. Location has a great impact on the success of many businesses. The existing works typically utilize the number of customers who are the Reverse Nearest Neighbors (RNN) of a business location to assess its goodness. While, with the prevalence of word-of-mouth marketing in social networks, a business can now exploit the social influence to attract enormous customers to visit it, even though it is not located in the popular but unaffordable business districts with the most RNNs. In this paper, we propose a novel Business Location Selection (BLS) approach to integrate the factors of both social influence and geographical distance. Firstly, we formally define a BLS model based on relative distance aware influence maximization in geo-social networks, where the goodness of a location is assessed by the maximum number of social network users it can influence via online propagation. To the best of our knowledge, it is the first BLS model that adopts the influence maximization techniques. Then, to speed up the selection, we present two sophisticated candidate location pruning strategies, and extend the Reverse Influence Sampling (RIS) algorithm to select seeds for multiple locations, thereby avoiding redundant computation. Lastly, we demonstrate the effectiveness and efficiency of our approach by conducting the experiments on three real geo-social networks.