User-Meal Interaction Learning for Meal Recommendation: A Reproducibility Study

Recommendation systems are important in web3.0 as a technology to achieve high-quality interaction between people and the web. 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. Common methods include collaborative filtering, attention-based and graph-based ones, etc. User-meal interaction learning is the core of a meal recommendation and its key feature is category-constrained. However, there is no work to compare and study different types of methods. Moreover, existing work does not make full use of category information in the meal recommendation. In this paper, we conduct a reproducibility study about user-meal interaction learning for meal recommendation. We reproduce seven state-of-the-art meal and general bundle recommendation models and re-implement two models with considering category information. Extensive experiments are conducted on two datasets with different user-meal interaction densities to explore the impact of data density on different types of user-meal interaction learning, and investigate the effectiveness of different category-wise implementations. The experimental results are instructive and beneficial to the development and application of meal recommendation research.