Zobrazeno 1 - 5
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pro vyhledávání: '"Cai, Xuheng"'
Publikováno v:
ICDE 2024
Graph augmentation with contrastive learning has gained significant attention in the field of recommendation systems due to its ability to learn expressive user representations, even when labeled data is limited. However, directly applying existing G
Externí odkaz:
http://arxiv.org/abs/2403.16656
Graph Neural Networks (GNNs) have demonstrated superior performance on various graph learning tasks, including recommendation, where they leverage user-item collaborative filtering signals in graphs. However, theoretical formulations of their capabil
Externí odkaz:
http://arxiv.org/abs/2308.11127
Self-supervised learning (SSL) has gained significant interest in recent years as a solution to address the challenges posed by sparse and noisy data in recommender systems. Despite the growing number of SSL algorithms designed to provide state-of-th
Externí odkaz:
http://arxiv.org/abs/2308.05697
Graph neural network (GNN) is a powerful learning approach for graph-based recommender systems. Recently, GNNs integrated with contrastive learning have shown superior performance in recommendation with their data augmentation schemes, aiming at deal
Externí odkaz:
http://arxiv.org/abs/2302.08191
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