Knowledge Transfer in Commercial Feature Extraction for the Retail Store Location Problem

Abstract

Location is the most important strategic decision in retailing. The location problem is markedly complex and multicriteria. One of the key factors to consider is the so-called balanced tenancy —i.e., the degree to which neighboring businesses complement each other. There are several network-based methodologies that formalize the notion of balanced tenancy by capturing the spatial interactions between different commercial sectors in cities. Some of these methodologies provide indices that have been successfully used as input features in location recommendation systems. However, from a predictive perspective, it is still unknown which of the indices provides best results. In this work, we analyze the performance of six of these indices on a set of nine Spanish cities. Our results show that the combined use of all of them in an ensemble model such as random forest significantly improves predictive accuracy. In addition, we explore the effect of knowledge transfer between cities from two different perspectives: 1) quantify how much the quality of solutions degrades when the balanced tenancy of a city is explored through the indices obtained from another city; 2) investigate the interest of network consensus approaches for knowledge transfer in retailing.