Abstract
“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.
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Notes
- 1.
Data for refugees of Turkey http://d4r.turktelekom.com.tr/.
- 2.
We assume that the expenditure function E also accounts for the quantity.
- 3.
We consider also features derived from others, like \( AL \) and \( AE \), since they might capture different aspects of the customer shopping behavior. Where needed, redundant features can be removed at the preprocessing stage preceding the training phase of the machine learning classifier.
- 4.
Notice that we are implicitly assuming that the food consumption habits of natives do not change over time. While not true in general, we empirically observed that it holds for the vast majority of customers in our data. Studying natives’ evolution in time is part of our future works.
- 5.
The source code of tinca is available here: https://github.com/riccotti/TINCA.
- 6.
- 7.
The 100 product groups are available in the shared repository. The grouping was performed manually to respect the implicit semantic meaning. Each product models on average 1.9 ± 2.0 categories of items of the UniCoop dataset. The largest product groups are those modeling “bread”, “fish”, and “vegetables”.
- 8.
- 9.
Pearson of 0.75 and Spearman of 0.78 in both cases with p-value < 0.0005.
- 10.
We leave to future works the study of the effect of other specific functions for time series clustering like dynamic time warping.
- 11.
For each group we emphasize the countries having the largest relative number of customers in that group normalized on the total number of customers from that specific country. Focusing on countries with larger absolute presence would be less interesting, as a few countries with overall very large presence (e.g. Romania, Switzerland and Germany) would simply overwhelm the others in all groups.
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Acknowledgment
This work is partially supported by the European Community H2020 programme under the funding schemes: H2020-INFRAIA-2019-1: Res. Infr. G.A. 871042 SoBigData++, G.A. 825619 AI4EU, G.A. 761758 Humane AI, and G.A. 780754 Track&Know. We thank UniCoop Tirreno for providing the data, and Roberto Zicaro for preliminary studies on the proposed methodology and analysis.
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Guidotti, R. et al. (2021). Measuring Immigrants Adoption of Natives Shopping Consumption with Machine Learning. In: Dong, Y., Ifrim, G., Mladenić, D., Saunders, C., Van Hoecke, S. (eds) Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12461. Springer, Cham. https://doi.org/10.1007/978-3-030-67670-4_23
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