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.

Food Recommendation in a Worksite Canteen

Carchiolo, Vincenza;Grassia, Marco;Longheu, Alessandro;Malgeri, Michele;Mangioni, Giuseppe
2021-01-01

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.
2021
Data Analysis
Recommendation System
Machine Learning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/710409
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