Medical Publication Recommendation Based on Cross-Modal Contrastive Learning Between Knowledge and Graph

[Objective] This study proposes a medical publication recommendation model that uses cross-modal information to improve recommendation accuracy. [Methods] First, the medical terminology system was employed to standardize label content and align image-text tags. Paired semantic labels were then utilized to align feature semantics between images and texts through contrastive learning. Based on the aligned semantic features, a cross-modal cross-attention mechanism was constructed, and user preferences for publications were predicted by analyzing their interest weights across different modalities. [Results] Comparative experiments with three state-of-the-art multimodal baseline models on two publication datasets showed that the proposed model achieved an average precision of 62.79%, F1-score of 53.62%, and NDCG of 61.17%, outperforming the baseline models in all metrics. [Limitations] Additional cold-start methods may be required for pre-training data containing only single-modality information. [Conclusions] The proposed model exhibits strong cross-modal feature fusion capabilities, effectively mitigating semantic gaps between modalities and improving the accuracy of medical publication recommendations. © 2025 Chinese Academy of Sciences. All rights reserved.