MealRec+: A Meal Recommendation Dataset with Meal-Course Affiliation for Personalization and Healthiness
Paper
Li, Ming, Li, Lin, Tao, Xiaohui and Huang, Jimmy Xiangji. 2024. "MealRec+: A Meal Recommendation Dataset with Meal-Course Affiliation for Personalization and Healthiness." 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’24). Washington DC, United States 14 - 18 Jul 2024 United States. Association for Computing Machinery (ACM). https://doi.org/10.1145/3626772.3657857
Paper/Presentation Title | MealRec+: A Meal Recommendation Dataset with Meal-Course Affiliation for Personalization and Healthiness |
---|---|
Presentation Type | Paper |
Authors | Li, Ming, Li, Lin, Tao, Xiaohui and Huang, Jimmy Xiangji |
Journal or Proceedings Title | Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’24) |
Journal Citation | pp. 564-574 |
Number of Pages | 11 |
Year | 2024 |
Publisher | Association for Computing Machinery (ACM) |
Place of Publication | United States |
ISBN | 9798400704314 |
Digital Object Identifier (DOI) | https://doi.org/10.1145/3626772.3657857 |
Web Address (URL) of Paper | https://dl.acm.org/doi/10.1145/3626772.3657857 |
Web Address (URL) of Conference Proceedings | https://dl.acm.org/doi/proceedings/10.1145/3626772 |
Conference/Event | 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’24) |
Event Details | 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’24) Parent ACM International Conference on Research and Development in Information Retrieval Delivery In person Event Date 14 to end of 18 Jul 2024 Event Location Washington DC, United States |
Abstract | Meal recommendation, as a typical health-related recommendation task, contains complex relationships between users, courses, and meals. Among them, meal-course affiliation associates user-meal and user-course interactions. However, an extensive literature review demonstrates that there is a lack of publicly available meal recommendation datasets including meal-course affiliation. Meal recommendation research has been constrained in exploring the impact of cooperation between two levels of interaction on personalization and healthiness. To pave the way for meal recommendation research, we introduce a new benchmark dataset called MealRec^+. Due to constraints related to user health privacy and meal scenario characteristics, the collection of data that includes both meal-course affiliation and two levels of interactions is impeded. Therefore, a simulation method is adopted to derive meal-course affiliation and user-meal interaction from the user's dining sessions simulated based on user-course interaction data. Then, two well-known nutritional standards are used to calculate the healthiness scores of meals. Moreover, we experiment with several baseline models, including separate and cooperative interaction learning methods. Our experiment demonstrates that cooperating the two levels of interaction in appropriate ways is beneficial for meal recommendations. The dataset is available on GitHub (https://github.com/WUT-IDEA/MealRecPlus). |
Keywords | AI for good; User-Course Affiliation; Meal Recommendation; Personalization; Healthiness |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460508. Information retrieval and web search |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Wuhan University of Technology, China |
University of Southern Queensland | |
York University, Canada |
Permalink -
https://research.usq.edu.au/item/z995y/mealrec-a-meal-recommendation-dataset-with-meal-course-affiliation-for-personalization-and-healthiness
18
total views0
total downloads4
views this month0
downloads this month