University of Oulu

M. Rostami, M. Oussalah and V. Farrahi, "A Novel Time-Aware Food Recommender-System Based on Deep Learning and Graph Clustering," in IEEE Access, vol. 10, pp. 52508-52524, 2022, doi: 10.1109/ACCESS.2022.3175317

A novel time-aware food recommender-system based on deep learning and graph clustering

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Author: Rostami, Mehrdad1; Oussalah, Mourad1,2; Farrahi, Vahid1,2
Organizations: 1Center for Machine Vision and Signal Analysis (CMVS), Faculty of ITEE, University of Oulu, 90014 Oulu, Finland
2Research Unit of Medical Imaging, Physics, and Technology, University of Oulu, 90014 Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.5 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-07-15


Food recommender-systems are considered an effective tool to help users adjust their eating habits and achieve a healthier diet. This paper aims to develop a new hybrid food recommender-system to overcome the shortcomings of previous systems, such as ignoring food ingredients, time factor, cold start users, cold start food items and community aspects. The proposed method involves two phases: food content-based recommendation and user-based recommendation. Graph clustering is used in the first phase, and a deep-learning based approach is used in the second phase to cluster both users and food items. Besides a holistic-like approach is employed to account for time and user-community related issues in a way that improves the quality of the recommendation provided to the user. We compared our model with a set of state-of-the-art recommender-systems using five distinct performance metrics: Precision, Recall, F1, AUC and NDCG. Experiments using dataset extracted from “” demonstrated that the developed food recommender-system performed best.

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Series: IEEE access
ISSN: 2169-3536
ISSN-E: 2169-3536
ISSN-L: 2169-3536
Volume: 10
Pages: 52508 - 52524
DOI: 10.1109/access.2022.3175317
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported in part by the Academy of Finland Profi5 DigiHealth under Project 326291; and in part by the Ministry of Education and Culture, Finland, under Grant OKM/20/626/2022.
Copyright information: © The Author(s) 2022. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see .