Document - Measuring immigrants adoption of natives shopping consumption with machine learning

2021

Conference article Open Access

Measuring immigrants adoption of natives shopping consumption with machine learning

Guidotti R., Nanni M., Giannotti F., Pedreschi D., Bertoli S., Speciale B., Rapoport H.

Data analysis Shopping behaviour Immigration

Tell me what you eat and I will tell you what you are". Jean Anthelme Brillat-Savarin was among the firsts to recognize the relationship between identity and food consumption. Food adoption choices are much less exposed to external judgment and social pressure than other individual behaviours, and can be observed over a long period. That makes them an interesting basis for, among other applications, studying the integration of immigrants from a food consumption viewpoint. Indeed, in this work we analyze immigrants' food consumption from shopping retail data for understanding if and how it converges towards those of natives. As core contribution of our proposal, we define a score of adoption of natives' consumption habits by an individual as the probability of being recognized as a native from a machine learning classifier, thus adopting a completely data-driven approach. We measure the immigrant's adoption of natives' consumption behavior over a long time, and we identify different trends. A case study on real data of a large nation-wide supermarket chain reveals that we can distinguish five main different groups of immigrants depending on their trends of native consumption adoption.

Source: ECML PKDD 2020 - Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 369–385, Ghent, Belgium, September 14-18, 2020

Publisher: Springer, Berlin, DEU

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BibTeX entry

@inproceedings{oai:it.cnr:prodotti:447161,
	title = {Measuring immigrants adoption of natives shopping consumption with machine learning},
	author = {Guidotti R. and Nanni M. and Giannotti F. and Pedreschi D. and Bertoli S. and Speciale B. and Rapoport H.},
	publisher = {Springer, Berlin, DEU},
	doi = {10.1007/978-3-030-67670-4_23},
	booktitle = {ECML PKDD 2020 - Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 369–385, Ghent, Belgium, September 14-18, 2020},
	year = {2021}
}