2024-03-28T11:19:24Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/63332022-11-29T13:32:40Zcom_10259_6336com_10259_6335com_10259.4_106com_10259_2604com_10259_3830com_10259_5086col_10259_6337col_10259_3832
Knowledge Transfer in Commercial Feature Extraction for the Retail Store Location Problem
Ahedo García, Virginia
Santos Martín, José Ignacio
Galán Ordax, José Manuel
Complex networks
Feature extraction
Recommendation systems
Retail location problem
Prediction
Knowledge transfer
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.
2022-01-18T08:02:49Z
2022-01-18T08:02:49Z
2022-01-18T08:02:49Z
2021-09
info:eu-repo/semantics/article
2169-3536
http://hdl.handle.net/10259/6333
10.1109/ACCESS.2021.3115712
eng
IEEE Access. 2021, V. 9, p. 132967-132979
http://dx.doi.org/10.1109/ACCESS.2021.3115712
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RED2018-102518-T/ES/SISTEMAS COMPLEJOS SOCIOTECNOLOGICOS
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118906GB-I00/ES/INTERACCIONES DINAMICAS DISTRIBUIDAS: PROTOCOLOS BEST EXPERIENCED PAYOFF Y SEPARACION ENDOGENA
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Atribución 4.0 Internacional
Institute of Electrical and Electronics Engineers (IEEE)