Competitive Analysis for Points of Interest
Aug 13, 20206 views
The competitive relationship of Points of Interest (POIs) refers to the,degree of competition between two POIs for business opportunities,from third parties in an urban area. Existing studies for competitive,analysis usually focus on mining competitive relationships of entities, such as companies or products, from textual data. However,,there are few studies which have a focus on competitive analysis for,POIs. Indeed, the growing availability of user behavior data about,POIs, such as POI reviews and human mobility data, enables a new,paradigm for understanding the competitive relationships among,POIs. To this end, in this paper, we study how to predict the POI,competitive relationship. Along this line, a very first challenge is,how to integrate heterogeneous user behavior data with the spatial,features of POIs. As a solution, we first build a heterogeneous POI,information network (HPIN) from POI reviews and map search,data. Then, we develop a graph neural network-based deep learning framework, named DeepR, for POI competitive relationship,prediction based on HPIN. Specifically, DeepR contains two components: a spatial adaptive graph neural network (SA-GNN) and a POI,pairwise knowledge extraction learning (PKE) model. The SA-GNN,is a novel GNN architecture with incorporating POI’s spatial information and location distribution by a specially designed spatial,oriented aggregation layer and spatial-dependency attentive propagation mechanism. In addition, PKE is devised to distill the POI,pairwise knowledge in HPIN being useful for relationship prediction into condensate vectors with relational graph convolution and,cross attention. Finally, extensive experiments on two real-world,datasets demonstrate the effectiveness of our method.