Category-Wise Meal Recommendation

Meal recommender system, as an application of bundle recommendation, aims to provide courses from specific categories (e.g., appetizer, main dish) that are enjoyed as a meal for a user. Existing bundle recommendation methods work on learning user preferences from user-bundle interactions to satisfy users’ information need. However, users in food scenarios may have different preferences for different course categories. It is a challenge to effectively consider course category constraints when predicting meals for users. To this end, we propose a model CMRec: Category-wise Meal Recommendation model. Specifically, our model first decomposes interactions and affiliations between users, meals, and courses according to category. Secondly, graph neural networks are utilized to learn category-wise user/meal representations. Then, the likelihood of user-meal interactions is estimated category by category. Finally, our model is trained by a category-wise enhanced Bayesian Personalized Ranking loss. CMRec outperforms state-of-the-art methods in terms of Recall@K and NDCG@K on two public datasets.