University of Oulu

Ahmadian, S., Rostami, M., Jalali, S.M.J. et al. Healthy Food Recommendation Using a Time-Aware Community Detection Approach and Reliability Measurement. Int J Comput Intell Syst 15, 105 (2022).

Healthy food recommendation using a time-aware community detection approach and reliability measurement

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Author: Ahmadian, Sajad1; Rostami, Mehrdad2; Jalali, Seyed Mohammad Jafar3;
Organizations: 1Faculty of Information Technology, Kermanshah University of Technology, Kermanshah, Iran
2Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, Oulu, Finland
3Institute for Intelligent Systems Research and Innovation, (IISRI), Deakin University, Melbourne, Australia
4Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 4.1 MB)
Persistent link:
Language: English
Published: Springer Nature, 2022
Publish Date: 2023-06-19


Food recommendation systems have been increasingly developed in online food services to make recommendations to users according to their previous diets. Although unhealthy diets may cause challenging diseases such as diabetes, cancer, and premature heart diseases, most of the developed food recommendation systems neglect considering health factors in their recommendation process. This emphasizes the importance of the reliability of the recommendation from the health content perspective. This paper proposes a new food recommendation system based on health-aware reliability measurement. In particular, we develop a time-aware community detection approach that groups users into disjoint sets and utilizes the identified communities as the nearest neighbors set in rating prediction. Then, a novel reliability measurement is introduced by considering both the health and accuracy criteria of predictions to evaluate the reliability of predicted ratings. Also, the unreliable predictions are recalculated by removing ineffective users from the nearest neighbors set. Finally, the recalculated predictions are utilized to generate a list of foods as recommendations. Different experiments on a crawled dataset demonstrate that the proposed method enhances the performance around 7.63%, 6.97%, 7.37%, 15.09%, and 16.17% based on precision, recall, F1, normalized discounted cumulative gain (NDCG), and health metrics, respectively, compared to the second-best model.

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Series: International journal of computational intelligence systems
ISSN: 1875-6891
ISSN-E: 1875-6883
ISSN-L: 1875-6891
Volume: 15
Issue: 1
Article number: 105
DOI: 10.1007/s44196-022-00168-4
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This research is connected to the DigiHealth-project, a strategic profiling project at the University of Oulu. The project is supported by the Academy of Finland (project number 326291) and the University of Oulu. Academy of Finland Profi5 on DigiHealth. Also, this work was supported in part by the Ministry of Education and Culture, Finland (OKM/20/626/2022). In addition, SA is supported by the Kermanshah University of Technology, Iran under grant number S/P/F/5.
Copyright information: © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit