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). https://doi.org/10.1007/s44196-022-00168-4
Healthy food recommendation using a time-aware community detection approach and reliability measurement
|Author:||Ahmadian, Sajad1; Rostami, Mehrdad2; Jalali, Seyed Mohammad Jafar3;|
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
|Online Access:||PDF Full Text (PDF, 4.1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023061956436
|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.
International journal of computational intelligence systems
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
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.
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