Abstract
The huge quantity of personal data stored by service providers registering customers daily life enables the analysis of individual fingerprints characterizing the customers’ behavioral profiles. We propose a methodological framework for recognizing residents, tourists and occasional shoppers among the customers of a retail market chain. We employ our recognition framework on a real massive dataset containing the shopping transactions of more than one million of customers, and we identify representative temporal shopping profiles for residents, tourists and occasional customers. Our experiments show that even though residents are about 33% of the customers they are responsible for more than 90% of the expenditure. We statistically validate the number of residents and tourists with national official statistics enabling in this way the adoption of our recognition framework for the development of novel services and analysis.
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Missing years are not available on the ISTAT website.
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Acknowledgement
This work is partially supported by the European Project SoBigData, 654024, http://www.sobigdata.eu. We thank UniCoop Tirreno for allowing us to analyze the data and to publish the results.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Guidotti, R., Gabrielli, L. (2018). Recognizing Residents and Tourists with Retail Data Using Shopping Profiles. In: Guidi, B., Ricci, L., Calafate, C., Gaggi, O., Marquez-Barja, J. (eds) Smart Objects and Technologies for Social Good. GOODTECHS 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 233. Springer, Cham. https://doi.org/10.1007/978-3-319-76111-4_35
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DOI: https://doi.org/10.1007/978-3-319-76111-4_35
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