Abstract: User interactions are often driven by latent, unobservable intentions, which are crucial for understanding and predicting behavior in recommendation systems. Previous work has attempted to ...
The proliferation of digital platforms has enabled fraudsters to deploy sophisticated camouflage techniques, such as multi-hop collaborative attacks, to evade detection. Traditional Graph Neural ...
Experiments were executed on NVIDIA A40 of 46068MiB memory in linux with torch==2.1.0+cu121, torch_geometric==2.3.1, torch-sparse==0.6.18+pt21cu121, and torchvision==0.16.0+cu121. The stkan is an ...
In this paper, we propose a disentangled contrastive learning method for recommendation, which explores latent factors underlying implicit intents for interactions. In particular, a graph structure ...
ABSTRACT: Accurate measurement of time-varying systematic risk exposures is essential for robust financial risk management. Conventional asset pricing models, such as the Fama-French three-factor ...
Abstract: In the context of data-driven decision-making, constructing accurate and well-generalized probabilistic forecasting models to handle dynamic, complex, and high-dimensional multivariate time ...
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