Semi-Supervised Regression for Evaluating Convenience Store Location

Location plays a very important role in the retail business due to its huge and long-term investment. In this paper, we propose a novel semisupervised regression model for evaluating convenience store location based on spatial data analysis. First, the input features for each convenience store can be extracted by analyzing the elements around it based on a geographic information system, and the turnover is used to evaluate its performance. Second, considering the practical application scenario, a manifold regularization model with one semi-supervised performance information constraint is provided. The promising experimental results in the real-world dataset demonstrate the effectiveness of the proposed approach in performance prediction of certain candidate locations for new convenience store opening.