# Prototypical Graph Contrastive Learning for Recommendation
## 1 Introduction  
Recommender systems can discover users' preferences towards items and recommend potentially related items to users, which is an important method for alleviating the problem of information overload [1]. As user-item interaction data continue to grow, traditional shallow models such as matrix factorization (MF) [2] and collaborative filtering (CF) [3] often struggle to capture complex high-order relationships. Graph neural networks (GNNs) [4], on the other hand, address this limitation by learning collaborative information between nodes based on existing interaction graphs and leveraging multi-layer structures to explore potential relationships in higher-order neighborhoods. However, the performance of GNNs heavily relies on the quality of the interaction graph. In recommendation tasks, the quality of the interaction graph is often constrained by several factors. First, data sparsity [5] caused by the limited number of user-item interactions poses a significant challenge. Second, noise introduced by user misoperations or short-term behavioral preferences further impacts the graph's reliability. Third, the lack of interaction records for certain long-tail items results in their under-representation. These issues not only weaken the expressive power of interaction graphs but also present substantial challenges for GNN-based recommendation methods. To address the aforementioned issues related to data sparsity [6], contrastive learning (CL) demonstrates strong adaptability by extracting latent features from unlabeled data. It aims to optimize the distribution of the embedding space by constructing positive and negative sample pairs, bringing samples with similar features closer together while pushing dissimilar samples apart. This enhances the model's representation capability for sparse data and further improves recommendation performance [7]. In graph-based contrastive learning, the primary methods for constructing contrastive views include structural augmentation and feature augmentation [8]. Structural augmentation is a strategy for constructing contrastive views by adding or removing nodes and edges in the user-item interaction graph [9]. Due to the large scale of the data, it is challenging to accurately remove noisy data or add effective interactions. As a result, existing structural augmentation methods typically adopt a random dropout approach. While removing noise or adding valid interactions can improve model performance to some extent, the dropout of critical nodes or edges may introduce additional noise. In more severe cases, such actions could disrupt the connectivity of the interaction graph, thereby undermining the ability of GNNs to capture high-order information. Ref. [10] indicates that various dropout strategies in graph structure have minimal impact on recommendation performance. Feature augmentation is a strategy that adds random noise or generates perturbations in the embedding space [11]. This approach modifies embeddings without altering the original graph structure to construct contrastive views. However, introducing excessive noise can make it difficult for the model to distinguish between meaningful feature information and irrelevant noise, thereby hindering its ability to preserve the core preference information of the features. Graph-based contrastive learning still faces the over-smoothing problem caused by deep graph convolution due to the application of graph neural networks on different views [12]. To address the limitations and challenges of graph-based contrastive learning in recommendation mentioned above, this paper proposes a simple yet effective method called ProtoRec, which employs prototype-based feature augmentation-guided contrastive views. ProtoRec distinguishes itself from existing methods that rely on random noise-based augmentation strategies by employing clustering methods to identify preference prototypes within the embedding space.