University of Oulu

Rostami, M., Farrahi, V., Ahmadian, S., Mohammad Jafar Jalali, S., & Oussalah, M. (2023). A novel healthy and time-aware food recommender system using attributed community detection. In Expert Systems with Applications (Vol. 221, p. 119719). Elsevier BV.

A novel healthy and time-aware food recommender system using attributed community detection

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Author: Rostami, Mehrdad1; Farrahi, Vahid1,2; Ahmadian, Sajad3;
Organizations: 1Center for Machine Vision and Signal Analysis (CMVS), Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
2Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
3Faculty of Information Technology, Kermanshah University of Technology, Kermanshah, Iran
4Institute for Intelligent Systems Research and Innovation, (IISRI), Deakin University, Waurn Ponds, Australia
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 4.9 MB)
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Language: English
Published: Elsevier, 2023
Publish Date: 2023-08-25


Food recommendation systems aim to provide recommendations according to a user’s diet, recipes, and preferences. These systems are deemed useful for assisting users in changing their eating habits towards a healthy diet that aligns with their preferences. Most previous food recommendation systems do not consider the health and nutrition of foods, which restricts their ability to generate healthy recommendations. This paper develops a novel health-aware food recommendation system that explicitly accounts for food ingredients, food categories, and the factor of time, predicting the user’s preference through time-aware collaborative filtering and a food ingredient content-based model. Based on the user's predicted preferences and the health factor of each food, our model provides final recommendations to the target user. The performance of our model was compared to several state-of-the-art recommender systems in terms of five distinct metrics: Precision, Recall, F1, AUC, and NDCG. Experimental analysis of datasets extracted from the websites and demonstrated that our proposed food recommender system performs well compared to previous food recommendation models.

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Series: Expert systems with applications
ISSN: 0957-4174
ISSN-E: 1873-6793
ISSN-L: 0957-4174
Volume: 221
Article number: 119719
DOI: 10.1016/j.eswa.2023.119719
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
217 Medical engineering
Funding: The project is supported by the Academy of Finland (project number 326291) and the University of Oulu Academy of Finland Profi5 on DigiHealth. Furthermore, SA is supported by the Kermanshah University of Technology, Iran under grant number S/P/F/5.
Dataset Reference: Data will be made available on request.
Copyright information: © 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (