AR 2 Net

Abstract

Business location selection is crucial to the success of businesses. Traditional approaches like manual survey investigate multiple factors, such as foot traffic, neighborhood structure, and available workforce, which are typically hard to measure. In this article, we propose to explore both satellite data (e.g., satellite images and nighttime light data) and urban data for business location selection tasks of various businesses. We extract discriminative features from the two kinds of data and perform empirical analysis to evaluate the correlation between extracted features and the business popularity of locations. A novel neural network approach named R 2 Net is proposed to learn deep interactions among features and predict the business popularity of locations. The proposed approach is trained with a regression-and-ranking combined loss function to preserve accurate popularity estimation and the ranking order of locations simultaneously. To support the location selection for multiple businesses, we propose an approach named AR 2 Net with three attention modules, which enable the approach to focus on different latent features according to business types. Comprehensive experiments on a real-world dataset demonstrate that the satellite features are effective and our models outperform the state-of-the-art methods in terms of four metrics.