<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Riccardo Guidotti</style></author><author><style face="normal" font="default" size="100%">Lorenzo Gabrielli</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Recognizing Residents and Tourists with Retail Data Using Shopping Profiles</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Smart Objects and Technologies for Social Good</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/chapter/10.1007/978-3-319-76111-4_35</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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 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.</style></abstract></record></records></xml>