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Knowledge-Enhanced Graph Contrastive Learning for Recommendations
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AI
DOI：10.1109/TMM.2025.3626976.png)
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Abstract
En 中文
Graph contrastive learning (GCL), which captures essential features from augmented graphs to address data sparsity issues, has recently demonstrated promising potential in improving recommendation performance. Most GCL-based recommendation methods learn consistent entity representations from user-item bipartite graphs through structural perturbations. However, these approaches impose an additional computational cost and have been shown to be insensitive to various graph augmentations, resulting in limited improvements in long-tail recommendation scenarios. To address this issue, we propose a novel framework for recommendation, Knowledge-Enhanced graph Contrastive Learning (KECL), which adopts knowledge graph-based embedding augmentation instead of graph enhancement to construct views for GCL. Specifically, we introduce a knowledge aggregation module with a heterogeneous attentive aggregator to capture relation heterogeneity in the knowledge graph. Furthermore, we propose a knowledge-based augmentation GCL model that adds knowledge-aware embeddings to the learned representations for more efficient representation-level augmentation. Extensive experiments on real-world datasets demonstrate that the knowledge-based augmentation approach effectively enhances recommendation performance and shows superiority over state-of-the-art methods.
Keywords:
Recommendation system
knowledge graph
contrastive learning
data augmentation
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