TL;DR
The Uncertainty-aware graph-based deep learning to rank (UGD-LTR) framework tackles two critical gaps in learning to rank, and can train independently in distributed environments, and graph feature extraction can exploit distributed graph algorithms, providing a clear roadmap for future high-performance computing (HPC) deployment.
Contrastive Conformal HGNN (CCF-HGNN) is proposed that accounts for uncertainty in hypergraph-based models by explicitly regularizing on the hypergraph structure for guaranteed and robust uncertainty estimates.
A pre-extractor model based on a hybrid architecture of rules and neural networks is introduced, which is used to identify long tail entities in the dataset and generate several candidate tail entities through relationships to improve the inference performance of the model.
The proposed LLM-augmented Multi-Graph Contrastive Learning (LLM-MGCL) is a multi-graph neural network that uses semantic and spatial information about items to extend the LightGCN backbone with two auxiliary item-item graphs that outperforms classical collaborative filtering, matrix factorization, and interaction-only...
TRWH (Text-driven Random Walk Heterogeneous Graph Neural Network), a novel framework that fuses LLM-generated textual profiles with heterogeneous graph structures through strategic random walk augmentation, is proposed.
The Adaptive Contrastive Knowledge Graph Embedding (ACKGE) framework, which uses the empirical mean and standard deviation of entity similarities to define probabilistic semantic bounds, achieves competitive performance against strong baselines in link prediction tasks, particularly in handling complex relational struc...
With the rapid development of information technology in military and complex system evaluation domains, the issue of "information overload" regarding evaluation data has become increasingly prominent. Traditional recommendation algorithms rely heavily on simple historical interactions and lack the capacity to capture s...
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