University of Oulu

Rostami, Mehrdad; Usman Muhammad; Saman Forouzandeh; Kamal Berahmand; Vahid Farrahi; and Mourad Oussalah. “An Effective Explainable Food Recommendation Using Deep Image Clustering and Community Detection.” Intelligent Systems with Applications 16 (November 2022): 200157.

An effective explainable food recommendation using deep image clustering and community detection

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Author: Rostami, Mehrdad1; Muhammad, Usman1; Forouzandeh, Saman2;
Organizations: 1Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, Finland
2School of Mathematics and Statistics, University of New South Wales, Sydney, NSW, Australia
3School of Computer Sciences, Science and Engineering Faculty, Queensland University of Technology (QUT), Australia
4Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.3 MB)
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Language: English
Published: Elsevier, 2022
Publish Date: 2023-05-05


In food diet communication domain, images convey important information to capture users’ attention beyond the traditional ingredient content, making it crucial to influence user-decision about the relevancy of a given diet. By using a deep learning-based image clustering method, this paper proposes an Explainable Food Recommendation system that uses the visual content of food to justify their recommendations. n the recommendation system. Especially, a new similarity score based on a tendency measure that quantifies the extent to which user community prefers a given food category is introduced and incorporated in the recommendation. Finally, a rule-based explainability is introduced to enhance transparency and interpretability of the recommendation outcome. Our experiments on a crawled dataset showed that the proposed method enhances recommendation quality in terms of precision, recall, F1, and Normalized Discounted Cumulative Gain (NDCG) by 7.35%, 6.70%, 7.32% and 14.38%, respectively, when compared to other existing methodologies for food recommendation. Besides ablation study is performed to demonstrate the technical soundness of the various components of our recommendation system.

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Series: Intelligent systems with applications
ISSN: 2667-3053
ISSN-E: 2667-3053
ISSN-L: 2667-3053
Volume: 16
Article number: 200157
DOI: 10.1016/j.iswa.2022.200157
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: The project is supported by the University of Oulu Academy of Finland Profi5 on Digihealth (project number 326291). Moreover, this work also was supported in part by the Ministry of Education and Culture, Finland (OKM/20/626/2022).
Dataset Reference: Data will be made available on request.
Copyright information: © 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.