Location prediction for facility placement by incorporating multi-characteristic information

Abstract

In the course of recommending locations for establishing new facilities on urban planning or commercial programming, the location prediction offers the optimal candidates, which maximizes the number of served customers or minimize customer inconvenience, therefore brings the maximum profits. In most existing studies, only the spatial-temporal features are recognized to evaluate the location popularity, where social relationships of customers, which are significant factors for popularity assessing, have been ignored. Additionally, current researches also fail to take capacities and categories of the facilities into consideration. To overcome the drawbacks, we introduce a novel model of Multi-characteristic Information based Top- $k$ Location Prediction (MITLP), it captures the spatio-temporal behaviors of customers based on historical trajectories, exploits the social relevancy from their friend relationships, as well as examines the category competitiveness of specific facilities thoroughly. Subsequently, by drawing on the feature evaluation and popularity quantization, MITLP will be implemented within a hybrid B-tree-liked recommending framework, Constrained Location and Social-Trajectory Clustered forest (CLSTC-forest), which can not only produce better performance in practice but also address the facility service constraints. Finally, extensive experiments conducted on real-world datasets demonstrate the higher efficiency and effectiveness of the proposed model.