  Self-supervised learning (SSL) has recently attracted significant attention
in the field of recommender systems. Contrastive learning (CL) stands out as a
major SSL paradigm due to its robust ability to generate self-supervised
signals. Mainstream graph contrastive learning (GCL)-based methods typically
implement CL by creating contrastive views through various data augmentation
techniques. Despite these methods are effective, we argue that there still
exist several challenges: i) Data augmentation (e.g., discarding edges or
adding noise) necessitates additional graph convolution (GCN) or modeling
operations, which are highly time-consuming and potentially harm the embedding
quality. ii) Existing CL-based methods use traditional CL objectives to capture
self-supervised signals. However, few studies have explored obtaining CL
objectives from more perspectives and have attempted to fuse the varying
signals from these CL objectives to enhance recommendation performance.
  To overcome these challenges, we propose a High-Order Fusion Graph
Contrastive Learning (HFGCL) framework for recommendation. Specifically, we
discards the data augmentations and instead high-order information from GCN
process to create contrastive views. Additionally, to integrate self-supervised
signals from various CL objectives, we propose an advanced CL objective. By
ensuring that positive pairs are distanced from negative samples derived from
both contrastive views, we effectively fuse self-supervised signals from
distinct CL objectives, thereby enhancing the mutual information between
positive pairs. Experimental results on three public datasets demonstrate the
superior effectiveness of HFGCL compared to the state-of-the-art baselines.
