FAGCL: frequency-based augmentation graph contrastive learning for recommendation

doi:10.1007/s10489-024-05857-5

2024-11-28

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DOI：10.1007/s10489-024-05857-5

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摘要

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Contrastive Learning (CL) has recently achieved remarkable performance in recommendation systems, especially in Graph Collaborative Filtering (GCF), due to its effective handling of data sparsity issues by comparing positive and negative sample pairs. In CL-based GCF models, those sample pairs can be created by various data augmentation methods, which can be typically divided into two main aspects: graph-based and feature-based. However, those methods are either slow to train or ignore graph structure during data augmentation. To solve those issues, in this paper, we propose a frequency-based augmentation graph contrastive learning model named FAGCL, which takes graph structure into account without a slow training process. To be specific, FAGCL consists of three key steps. First, we propose a frequency-based data augmentation method to reconstruct the user-item interaction graph and get sample pairs for contrastive learning, which is fast to operate and can filter out some high-frequency graph signals that may lower model's accuracy. Second, to improve efficiency, we propose an optimized GNNs forward propagation process for CL-based GCF models based on the first step. Third, to avoid extra forward/backward propagation processes, we adopt the one-encoder framework, which combines recommendation and contrastive learning tasks in the same pipeline instead of separating them. Extensive experiments on three benchmark datasets demonstrate that the proposed model FAGCL has the fastest speed of data augmentation and training, and outperforms other CL-based GCF models in accuracy in most cases.

Keyword:

Collaborative filtering

Recommendation system

Graph neural networks

Contrastive learning

Data augmentation