An effective explainable food recommendation using deep image clustering and community detection
Rostami, Mehrdad; Muhammad, Usman; Forouzandeh, Saman; Berahmand, Kamal; Farrahi, Vahid; Oussalah, Mourad (2022-11-23)
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. https://doi.org/10.1016/j.iswa.2022.200157
© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.
https://creativecommons.org/licenses/by-nc-nd/4.0/
https://urn.fi/URN:NBN:fi-fe2023050541121
Tiivistelmä
Abstract
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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