  Bundle recommendation systems aim to recommend a bundle of items for a user
to consider as a whole. They have become a norm in modern life and have been
applied to many real-world settings, such as product bundle recommendation,
music playlist recommendation and travel package recommendation. However,
compared to studies of bundle recommendation approaches in areas such as online
shopping and digital music services, research on meal recommendations for
restaurants in the hospitality industry has made limited progress, due largely
to the lack of high-quality benchmark datasets. A publicly available dataset
specialising in meal recommendation research for the research community is in
urgent demand. In this paper, we introduce a meal recommendation dataset
(MealRec) that aims to facilitate future research. MealRec is constructed from
the user review records of Allrecipe.com, covering 1,500+ users, 7,200+ recipes
and 3,800+ meals. Each recipe is described with rich information, such as
ingredients, instructions, pictures, category and tags, etc; and each meal is
three-course, consisting of an appetizer, a main dish and a dessert.
Furthermore, we propose a category-constrained meal recommendation model that
is evaluated through comparative experiments with several state-of-the-art
bundle recommendation methods on MealRec. Experimental results confirm the
superiority of our model and demonstrate that MealRec is a promising testbed
for meal recommendation related research.
  The MealRec dataset and the source code of our proposed model are available
at https://github.com/WUT-IDEA/MealRec for access and reproducibility.
