A Novel Time-Aware Food Recommender-System Based on Deep Learning and Graph Clustering
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 star...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9775081/ |
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| author | Mehrdad Rostami Mourad Oussalah Vahid Farrahi |
| author_facet | Mehrdad Rostami Mourad Oussalah Vahid Farrahi |
| author_sort | Mehrdad Rostami |
| collection | DOAJ |
| description | 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 “Allrecipes.com” demonstrated that the developed food recommender-system performed best. |
| format | Article |
| id | doaj-art-83a124bdc05441b09ae70f821d2139ec |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-83a124bdc05441b09ae70f821d2139ec2025-08-20T01:59:16ZengIEEEIEEE Access2169-35362022-01-0110525085252410.1109/ACCESS.2022.31753179775081A Novel Time-Aware Food Recommender-System Based on Deep Learning and Graph ClusteringMehrdad Rostami0https://orcid.org/0000-0001-5710-217XMourad Oussalah1https://orcid.org/0000-0002-4422-8723Vahid Farrahi2https://orcid.org/0000-0001-8355-8488Center for Machine Vision and Signal Analysis (CMVS), Faculty of ITEE, University of Oulu, Oulu, FinlandCenter for Machine Vision and Signal Analysis (CMVS), Faculty of ITEE, University of Oulu, Oulu, FinlandCenter for Machine Vision and Signal Analysis (CMVS), Faculty of ITEE, University of Oulu, Oulu, FinlandFood 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 “Allrecipes.com” demonstrated that the developed food recommender-system performed best.https://ieeexplore.ieee.org/document/9775081/Recommender-systemfood recommendationhealthcaredeep learninggraph clustering |
| spellingShingle | Mehrdad Rostami Mourad Oussalah Vahid Farrahi A Novel Time-Aware Food Recommender-System Based on Deep Learning and Graph Clustering IEEE Access Recommender-system food recommendation healthcare deep learning graph clustering |
| title | A Novel Time-Aware Food Recommender-System Based on Deep Learning and Graph Clustering |
| title_full | A Novel Time-Aware Food Recommender-System Based on Deep Learning and Graph Clustering |
| title_fullStr | A Novel Time-Aware Food Recommender-System Based on Deep Learning and Graph Clustering |
| title_full_unstemmed | A Novel Time-Aware Food Recommender-System Based on Deep Learning and Graph Clustering |
| title_short | A Novel Time-Aware Food Recommender-System Based on Deep Learning and Graph Clustering |
| title_sort | novel time aware food recommender system based on deep learning and graph clustering |
| topic | Recommender-system food recommendation healthcare deep learning graph clustering |
| url | https://ieeexplore.ieee.org/document/9775081/ |
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