  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. Furthermore, in response to the less healthy recommendation
phenomenon found in the experiment, we explore methods to enhance the
healthiness of meal recommendations. The dataset is available on GitHub
(https://github.com/WUT-IDEA/MealRecPlus).
