Deep Semantic-Aligned Heterogeneous Graph Neural Network for Evaluation Data Recommendation
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.