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Found 4 papers, 1 papers with code
GAIM: Attacking Graph Neural Networks via Adversarial Influence Maximization
In our approach, we unify the selection of the target node and the construction of feature perturbations into a single optimization problem, ensuring a unique and consistent feature perturbation for each target node.
SimCE: Simplifying Cross-Entropy Loss for Collaborative Filtering
The learning objective is integral to collaborative filtering systems, where the Bayesian Personalized Ranking (BPR) loss is widely used for learning informative backbones.
Enhancing Transformers without Self-supervised Learning: A Loss Landscape Perspective in Sequential Recommendation
Despite their success, Transformer-based models often require the optimization of a large number of parameters, making them difficult to train from sparse data in sequential recommendation.
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