Automated and personalized meal plan generation and relevance scoring using a multi-factor adaptation of the transportation problem
Establishing a healthy lifestyle has become a very important aspect in people’s lives. The latter requires maintaining a healthy nutrition by considering the nature and quantity of foods being consumed, allowing to regulate one’s intake and consumption of calories and nutrients. As a result, people...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | |
| التنسيق: | article |
| منشور في: |
2021
|
| الوصول للمادة أونلاين: | http://hdl.handle.net/10725/15986 https://doi.org/10.1007/s00500-021-06400-1 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://link.springer.com/article/10.1007/s00500-021-06400-1 |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513471838683136 |
|---|---|
| author | Salloum, George |
| author2 | Tekli, Joe |
| author2_role | author |
| author_facet | Salloum, George Tekli, Joe |
| author_role | author |
| dc.creator.none.fl_str_mv | Salloum, George Tekli, Joe |
| dc.date.none.fl_str_mv | 2021-11-10 2022 2024-08-14T11:11:07Z 2024-08-14T11:11:07Z |
| dc.identifier.none.fl_str_mv | 1432-7643 http://hdl.handle.net/10725/15986 https://doi.org/10.1007/s00500-021-06400-1 Salloum, G., & Tekli, J. (2022). Automated and personalized meal plan generation and relevance scoring using a multi-factor adaptation of the transportation problem. Soft Computing, 26(5), 2561-2585. http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://link.springer.com/article/10.1007/s00500-021-06400-1 |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | Soft Computing |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.title.none.fl_str_mv | Automated and personalized meal plan generation and relevance scoring using a multi-factor adaptation of the transportation problem |
| dc.type.none.fl_str_mv | Article info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | Establishing a healthy lifestyle has become a very important aspect in people’s lives. The latter requires maintaining a healthy nutrition by considering the nature and quantity of foods being consumed, allowing to regulate one’s intake and consumption of calories and nutrients. As a result, people reach out for nutrition experts which services are costly, time-consuming, and not readily available. While various e-solutions have been developed to perform meal planning, yet most of them lack a completely automated process and require domain expert intervention at different stages of the recommendation process (e.g., identifying macronutrient distribution, providing pre-defined meal plans, or combining recommended foods into meal structures). In addition, most solutions focus on fulfilling the patients’ nutrition requirements (in terms of caloric intake and macronutrients) while disregarding other relevant factors such as patient food preferences, food variety, food-meal compatibility, and inter-food compatibility. Hence, there is a need for an automated solution to produce a full-fledged meal plan from scratch, based on a recommended caloric intake and considering multiple factors. In this study, we introduce a novel solution titled MPG for automated Meal Plan Generation recommendations, designed based on an adaptation of the transportation optimization problem to simulate the “human thought process” involved in generating daily meal plans. MPG allows to: (i) generate plans which fulfill a recommended caloric intake, given a set of available foods, while (ii) personalizing the plans following patient chosen factors (e.g., food preferences, variety, and compatibility), and (iii) evaluating the relevance of the produced plans following patient preferences. We have conducted various experiments involving 9 human testers and 124 meal plans to test the performance of MPG. Results highlight MPG’s effectiveness in producing “healthy” and personalized meal plans while complying with the testers’ preferences. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | LAURepo_e2cd42201a474a9d5da443af92220db6 |
| identifier_str_mv | 1432-7643 Salloum, G., & Tekli, J. (2022). Automated and personalized meal plan generation and relevance scoring using a multi-factor adaptation of the transportation problem. Soft Computing, 26(5), 2561-2585. |
| language_invalid_str_mv | en |
| network_acronym_str | LAURepo |
| network_name_str | Lebanese American University repository |
| oai_identifier_str | oai:laur.lau.edu.lb:10725/15986 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Automated and personalized meal plan generation and relevance scoring using a multi-factor adaptation of the transportation problemSalloum, GeorgeTekli, JoeEstablishing a healthy lifestyle has become a very important aspect in people’s lives. The latter requires maintaining a healthy nutrition by considering the nature and quantity of foods being consumed, allowing to regulate one’s intake and consumption of calories and nutrients. As a result, people reach out for nutrition experts which services are costly, time-consuming, and not readily available. While various e-solutions have been developed to perform meal planning, yet most of them lack a completely automated process and require domain expert intervention at different stages of the recommendation process (e.g., identifying macronutrient distribution, providing pre-defined meal plans, or combining recommended foods into meal structures). In addition, most solutions focus on fulfilling the patients’ nutrition requirements (in terms of caloric intake and macronutrients) while disregarding other relevant factors such as patient food preferences, food variety, food-meal compatibility, and inter-food compatibility. Hence, there is a need for an automated solution to produce a full-fledged meal plan from scratch, based on a recommended caloric intake and considering multiple factors. In this study, we introduce a novel solution titled MPG for automated Meal Plan Generation recommendations, designed based on an adaptation of the transportation optimization problem to simulate the “human thought process” involved in generating daily meal plans. MPG allows to: (i) generate plans which fulfill a recommended caloric intake, given a set of available foods, while (ii) personalizing the plans following patient chosen factors (e.g., food preferences, variety, and compatibility), and (iii) evaluating the relevance of the produced plans following patient preferences. We have conducted various experiments involving 9 human testers and 124 meal plans to test the performance of MPG. Results highlight MPG’s effectiveness in producing “healthy” and personalized meal plans while complying with the testers’ preferences.Published2024-08-14T11:11:07Z2024-08-14T11:11:07Z20222021-11-10Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1432-7643http://hdl.handle.net/10725/15986https://doi.org/10.1007/s00500-021-06400-1Salloum, G., & Tekli, J. (2022). Automated and personalized meal plan generation and relevance scoring using a multi-factor adaptation of the transportation problem. Soft Computing, 26(5), 2561-2585.http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.phphttps://link.springer.com/article/10.1007/s00500-021-06400-1enSoft Computinginfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/159862024-08-14T11:11:22Z |
| spellingShingle | Automated and personalized meal plan generation and relevance scoring using a multi-factor adaptation of the transportation problem Salloum, George |
| status_str | publishedVersion |
| title | Automated and personalized meal plan generation and relevance scoring using a multi-factor adaptation of the transportation problem |
| title_full | Automated and personalized meal plan generation and relevance scoring using a multi-factor adaptation of the transportation problem |
| title_fullStr | Automated and personalized meal plan generation and relevance scoring using a multi-factor adaptation of the transportation problem |
| title_full_unstemmed | Automated and personalized meal plan generation and relevance scoring using a multi-factor adaptation of the transportation problem |
| title_short | Automated and personalized meal plan generation and relevance scoring using a multi-factor adaptation of the transportation problem |
| title_sort | Automated and personalized meal plan generation and relevance scoring using a multi-factor adaptation of the transportation problem |
| url | http://hdl.handle.net/10725/15986 https://doi.org/10.1007/s00500-021-06400-1 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://link.springer.com/article/10.1007/s00500-021-06400-1 |