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Found 4 papers, 4 papers with code
Graph Augmentation for Recommendation
To address these challenges, we propose a principled framework called GraphAug.
How Expressive are Graph Neural Networks in Recommendation?
Most existing works adopt the graph isomorphism test as the metric of expressiveness, but this graph-level task may not effectively assess a model's ability in recommendation, where the objective is to distinguish nodes of different closeness.
SSLRec: A Self-Supervised Learning Framework for Recommendation
Our SSLRec platform covers a comprehensive set of state-of-the-art SSL-enhanced recommendation models across different scenarios, enabling researchers to evaluate these cutting-edge models and drive further innovation in the field.
LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation
In this paper, we propose a simple yet effective graph contrastive learning paradigm LightGCL that mitigates these issues impairing the generality and robustness of CL-based recommenders.
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