Food Recommendation in a Worksite Canteen

Vincenza Carchiolo; Marco Grassia; Alessandro Longheu; Michele Malgeri; Giuseppe Mangioni

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Paper

Food Recommendation in a Worksite Canteen

Topics: AI-based Computing, AI-based Analysis; Big Data Predictive Modeling; Cultural Models; Information Systems

In Proceedings of the 6th International Conference on Complexity, Future Information Systems and Risk COMPLEXIS - Volume 1, 117-124, 2021

Authors: Vincenza Carchiolo ¹ ; Marco Grassia ² ; Alessandro Longheu ² ; Michele Malgeri ² and Giuseppe Mangioni ²

Affiliations: ¹ Dip. Matematica e Informatica, Universitá di Catania, Italy ; ² Dip. Ingegneria Elettrica Elettronica Informatica, University of Catania, Italy

Keyword(s): Data Analysis, Recommendation System, Machine Learning.

Abstract: Recommendation systems tackle with information overload to assist people in finding their best choice according to their preferences and past behaviour. This occurred in many contexts, including the food sector where culinary inspiration, sales increase or healthy advice motivate the adoption of such a system. In this paper we propose a canteen food recommendation system for workers operating at an innovation hub including more than 20 companies. The system leverages a 30 months data set of past choices, and adopts a content based and a collaborative filtering approach for canteen users, suggesting them with dishes chosen by other similar users. First results for frequent as well as occasional canteen visitors are encouraging to validate the proposed approach.

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Paper citation in several formats:

Carchiolo, V.; Grassia, M.; Longheu, A.; Malgeri, M. and Mangioni, G. (2021). Food Recommendation in a Worksite Canteen. In Proceedings of the 6th International Conference on Complexity, Future Information Systems and Risk - COMPLEXIS; ISBN 978-989-758-505-0; ISSN 2184-5034, SciTePress, pages 117-124. DOI: 10.5220/0010502401170124

@conference{complexis21,
author={Vincenza Carchiolo. and Marco Grassia. and Alessandro Longheu. and Michele Malgeri. and Giuseppe Mangioni.},
title={Food Recommendation in a Worksite Canteen},
booktitle={Proceedings of the 6th International Conference on Complexity, Future Information Systems and Risk - COMPLEXIS},
year={2021},
pages={117-124},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010502401170124},
isbn={978-989-758-505-0},
issn={2184-5034},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Complexity, Future Information Systems and Risk - COMPLEXIS
TI - Food Recommendation in a Worksite Canteen
SN - 978-989-758-505-0
IS - 2184-5034
AU - Carchiolo, V.
AU - Grassia, M.
AU - Longheu, A.
AU - Malgeri, M.
AU - Mangioni, G.
PY - 2021
SP - 117
EP - 124
DO - 10.5220/0010502401170124
PB - SciTePress