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 graph neural network baselines.
Graph Neural Networks (GNNs) and Large Language Models (LLMs) have each advanced recommendation systems by modeling structural and semantic signals, respectively. However, integrating their complementary strengths remains challenging, particularly in sparse settings where maintaining semantic precision is critical. We propose 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. TRWH consists of three core components: (1) Embedding Creation, which produces user and item representations using both Word2Vec and LLM-based profiling; (2) a Heterogeneous Graph Neural Network (HeteroGNN) that propagates information across multi-relational edges; and (3) Random Walk-based Path Construction, which enriches sparse graphs with second-order user-user and item-item links. Experiments on the Amazon-2023 Fashion (2M users, 825K items) and Beauty (631K users, 112K items) datasets demonstrate that TRWH achieves substantial performance gains over state-of-the-art methods, including 80.0% RMSE and 52.6% MAE reductions on Fashion, and 25.7% and 10.8% improvements on Beauty. Notably, while random walks improve performance with traditional embeddings, they can dilute the nuanced representations learned by LLMs, underscoring the importance of adaptive integration strategies.
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 structures.
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 semantic characteristics of evaluation texts, nor can they fully exploit high-order structural information across heterogeneous networks. To address these challenges, this paper proposes an evaluation data recommendation method based on a Deep Semantic-Aligned Heterogeneous Graph Neural Network (DeSe-HGNN). The core innovation lies in the bidirectional coupling of deep semantic alignment and dynamic topology optimization. Unlike traditional models that treat text and graphs as isolated inputs, DeSe-HGNN introduces an implicit similarity infiltration mechanism. First, a dual-tower model incorporating BERT and a Convolutional Neural Network (CNN) is developed to extract deep semantic-level features from evaluation text data, thereby constructing initial data-data affinity graphs. Second, a continuous graph learner is employed to dynamically optimize the topology of both user-user and data-data subgraphs, effectively mitigating noise and resolving data sparsity in the initial graph. Finally, a multi-layer Heterogeneous Graph Neural Network (HGNN) is architecture-designed to aggregate high-order cross-semantic representations along multiple meta-paths, combined with an attention mechanism for rating prediction. Experimental rationales and theoretical analyses demonstrate that the proposed model provides superior interpretability and enhanced recommendation accuracy in professional evaluation domains.
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