A Novel Explainable and Health-aware Food Recommender System

Merhrdad Rostami, Vahid Farahi, Vahid Farahi, Kamal Berahmand, Saman Forouzandeh, Sajad Ahmadian, Mourad Oussalah, Mourad Oussalah

2022

Abstract

Food recommendation systems are increasingly being used by online food services to make recommendations. Health factors are often ignored in most of these systems, despite the fact that unhealthy diets are connected to a wide range of non-communicable diseases. Furthermore, if users do not receive compelling explanations about the recommended healthy foods, they may become hesitant to try them. In this paper, a novel explainable and health-aware food recommender system is developed to address these challenges. For this purpose, user’s preferences and food health factors are taken into account simultaneously and then a rule-based mechanism is employed for final healthy and explainable recommendations. Five performance metrics were used to compare our system with different new recommender systems. Using a dataset crawled from ”Allrecipes.com”, the proposed model is shown to perform best.

Paper Citation

in Harvard Style

Rostami M., Farahi V., Berahmand K., Forouzandeh S., Ahmadian S. and Oussalah M. (2022). A Novel Explainable and Health-aware Food Recommender System. In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 3: KMIS; ISBN 978-989-758-614-9, SciTePress, pages 208-215. DOI: 10.5220/0011561700003335

in Bibtex Style

@conference{kmis22,
author={Merhrdad Rostami and Vahid Farahi and Kamal Berahmand and Saman Forouzandeh and Sajad Ahmadian and Mourad Oussalah},
title={A Novel Explainable and Health-aware Food Recommender System},
booktitle={Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 3: KMIS},
year={2022},
pages={208-215},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011561700003335},
isbn={978-989-758-614-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 3: KMIS
TI - A Novel Explainable and Health-aware Food Recommender System
SN - 978-989-758-614-9
AU - Rostami M.
AU - Farahi V.
AU - Berahmand K.
AU - Forouzandeh S.
AU - Ahmadian S.
AU - Oussalah M.
PY - 2022
SP - 208
EP - 215
DO - 10.5220/0011561700003335
PB - SciTePress