## 1 Introduction  
The growth of massive amounts of data make it difficult for users to select interactive items that meet their requirements, and this is no exception when it comes to recipe selection. Recipe websites (e.g., Allrecipes, Food.com (accessed on 4 November 2022), etc.) provide users with a rich selection of recipe content, reviews, categories, etc. However, with the huge number of users and the entries matching problem, it is no doubt a difficult challenge to recommend recipes that match users' personalized preferences from the large number of matching recipes. Therefore, personalized recipe recommendation aims to help users select recipes that meet their personal requirements from the information explosion of items. Indeed, existing research on recommender systems can be divided into three categories: collaborative filtering-based methods (CF) [1; 2], content-based methods (CB) [3; 4] and hybrid methods [5; 6]. Collaborative filtering-based recommendation methods learn representations of users and items from users' historical interactions, which are often easy to implement but lead to coarse-grained representations. Content-based recommendation methods construct representations of items by exploring their content information (e.g., photos, attributes, etc. ); these methods ignore users' interests and cannot satisfy personalization requirements. Hybrid recommendation methods achieved good results in many application scenarios by combining models to address the shortcomings of different recommendation methods. There also exists much research on the task of recipe recommendation that experiments with three recommendation methods [4; 5]. However, there are challenges in recipe recommendation that differ from the general recommendation task: (1) the target of recipe recommendation is non-atomic items, and the ingredients, flavors, cooking methods and other attributes of recipes are crucial to users' choices. This means that the recipe recommendation task should pay more attention to high-order relationship structure information and semantic information between nodes. (2) The personalized preferences of users in recipe recommendation are more complex. Two different users may have different reasons for choosing the same recipe, and there are differences in the taste of two recipes with similar ingredients. So, the personalized recipe recommendation task has higher quality requirements for fine-grained embedding representation of users and recipe nodes. Graph neural networks (GNNs) were successfully applied to various fields in recommendation, such as: travel [7], video [3], etc., and received a lot of attention from researchers. Graph structure data can better represent the rich relationships between nodes, and at the same time, the powerful information aggregation capability of GNNs can enable the transfer of some features of neighboring nodes to the target node for a higher quality node representation. GNN-based models sometimes achieve an increase in the accuracy of recommendations at the cost of amplifying biases in the data and producing unfair recommendations [8], which brings new ideas for recipe recommendations. However, there are many difficulties in applying GNN to recipe recommendation as well. Among them, the long-tailed distribution of heterogeneous graph entities may cause the loss of the personalized value of recommendation results because the sparsity of supervised features may lead to the over-smoothing of node embeddings. For this reason, the recipe recommendation task needs to make full use of the limited interaction and relevance information for personalized recommendations. In this paper, we propose a new multi-level knowledge-aware contrastive learning network (MKCLN) for personalized recipe recommendation.