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学者姓名：檀彦超
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Abstract ：
Electronic Health Records (EHRs) are pivotal for healthcare prediction tasks, offering rich patient data such as symptoms, diagnoses, and treatments. Recent advances in Retrieval-Augmented Generation (RAG) have gained attention due to the ability to retrieve relevant information from medical sources to improve EHR-based predictions. However, existing RAG approaches for medical applications often struggle with flat data representations, which fail to capture the complex inter-dependencies among medical entities, leading to fragmented and verbose responses. In this work, we propose MedGR, a novel framework for healthcare prediction that incorporates graph-based clinical text indexing with a dual-level medical retrieval architecture. By leveraging graph-structured knowledge, we synthesize information from multiple sources into coherent and contextually enriched responses in an efficient manner. The experiment results showed that our medical RAG framework achieved high precision performance on both diagnosis and medical code prediction.
Keyword ：
Electronic Health Record Electronic Health Record LLMs LLMs Medical Retrieval Medical Retrieval RAG RAG
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| GB/T 7714 | Tan, Y. , Zhang, J. , Zhuang, J. et al. Medical Retrieval-Augmentation Generation Framework for Healthcare Prediction [J]. | Studies in health technology and informatics , 2025 , 329 : 628-632 . |
| MLA | Tan, Y. et al. "Medical Retrieval-Augmentation Generation Framework for Healthcare Prediction" . | Studies in health technology and informatics 329 (2025) : 628-632 . |
| APA | Tan, Y. , Zhang, J. , Zhuang, J. , Ma, G. , Yang, C. . Medical Retrieval-Augmentation Generation Framework for Healthcare Prediction . | Studies in health technology and informatics , 2025 , 329 , 628-632 . |
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In the dynamic environment of multimedia-sharing platforms like X (formerly known as Twitter) and TikTok, multimedia recommendation systems have been widely used to help users discover items of interest. However, traditional approaches often fall short, when the item modalities are incomplete, a common issue in realworld scenarios. To this end, we introduce the unified heterogeneous Hypergraph construction for the Incomplete multimedia REcommendation (HIRE), a novel framework designed to jointly learn a heterogeneous hypergraph and perform accurate recommendations under incomplete scenarios. HIRE first initializes the unified heterogeneous hypergraph for modality completion and employs self-supervised learning aligned with the contrastive text-centered view for multimedia recommendation. Such integration effectively handles the challenges posed by incomplete modalities, leading to improved recommendation accuracy. Furthermore, we find that the hypergraph directly learned from the HIRE is a dense structure which can be inaccurate and coarse. Therefore, we devise the HIRE framework with Sparse constraint named HIRES, which uniquely integrates optimal transport and a degrees 2,1-norm to refine the hypergraph structure. Our extensive experiments across various datasets demonstrate the superiority of HIRES in addressing incomplete modalities, establishing it as a powerful tool for personalized multimedia recommendations.
Keyword ：
Heterogeneous Hypergraph Heterogeneous Hypergraph Incomplete Multimedia Recommendation Incomplete Multimedia Recommendation Multimodal Representation Learning Multimodal Representation Learning Sparse Constraint Sparse Constraint
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| GB/T 7714 | Lin, Zhenghong , Tan, Yanchao , Chen, Jiamin et al. Unified Heterogeneous Hypergraph Construction for Incomplete Multimedia Recommendation [J]. | ACM TRANSACTIONS ON INFORMATION SYSTEMS , 2025 , 43 (5) . |
| MLA | Lin, Zhenghong et al. "Unified Heterogeneous Hypergraph Construction for Incomplete Multimedia Recommendation" . | ACM TRANSACTIONS ON INFORMATION SYSTEMS 43 . 5 (2025) . |
| APA | Lin, Zhenghong , Tan, Yanchao , Chen, Jiamin , Zhang, Hengyu , Chen, Chaochao , Wang, Shiping et al. Unified Heterogeneous Hypergraph Construction for Incomplete Multimedia Recommendation . | ACM TRANSACTIONS ON INFORMATION SYSTEMS , 2025 , 43 (5) . |
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Multi-view learning methods leverage multiple data sources to enhance perception by mining correlations across views, typically relying on predefined categories. However, deploying these models in real-world scenarios presents two primary openness challenges. 1) Lack of Interpretability: The integration mechanisms of multi-view data in existing black-box models remain poorly explained; 2) Insufficient Generalization: Most models are not adapted to multi-view scenarios involving unknown categories. To address these challenges, we propose OpenViewer, an openness-aware multi-view learning framework with theoretical support. This framework begins with a Pseudo-Unknown Sample Generation Mechanism to efficiently simulate open multi-view environments and previously adapt to potential unknown samples. Subsequently, we introduce an Expression-Enhanced Deep Unfolding Network to intuitively promote interpretability by systematically constructing functional prior-mapping modules and effectively providing a more transparent integration mechanism for multi-view data. Additionally, we establish a Perception-Augmented Open-Set Training Regime to significantly enhance generalization by precisely boosting confidences for known categories and carefully suppressing inappropriate confidences for unknown ones. Experimental results demonstrate that OpenViewer effectively addresses openness challenges while ensuring recognition performance for both known and unknown samples. Copyright © 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Keyword ：
Deep learning Deep learning Multi-task learning Multi-task learning
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| GB/T 7714 | Du, Shide , Fang, Zihan , Tan, Yanchao et al. OpenViewer: Openness-Aware Multi-View Learning [C] . 2025 : 16389-16397 . |
| MLA | Du, Shide et al. "OpenViewer: Openness-Aware Multi-View Learning" . (2025) : 16389-16397 . |
| APA | Du, Shide , Fang, Zihan , Tan, Yanchao , Wang, Changwei , Wang, Shiping , Guo, Wenzhong . OpenViewer: Openness-Aware Multi-View Learning . (2025) : 16389-16397 . |
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Recently, heterogeneous graphs have attracted widespread attention as a powerful and practical superclass of traditional homogeneous graphs, which reflect the multi-type node entities and edge relations in the real world. Most existing methods adopt meta-path construction as the mainstream to learn long-range heterogeneous semantic messages between nodes. However, such schema constructs the node-wise correlation by connecting nodes via pre-computed fixed paths, which neglects the diversities of meta-paths on the path type and path range. In this paper, we propose a meta-path-based semantic embedding schema, which is called Heterogeneous Graph Embedding with Dual Edge Differentiation (HGE-DED) to adequately construct flexible meta-path combinations thus learning the rich and discriminative semantic of target nodes. Concretely, HGEDED devises a Multi-Type and multi-Range Meta-Path Construction (MTR-MP Construction), which covers the comprehensive exploration of meta-path combinations from path type and path range, expressing the diversity of edges at more fine-grained scales. Moreover, HGE-DED designs the semantics and meta-path joint guidance, constructing a hierarchical short- and long-range relation adjustment, which constrains the path learning as well as minimizes the impact of edge heterophily on heterogeneous graphs. Experimental results on four benchmark datasets demonstrate the effectiveness of HGE-DED compared with state-of-the-art methods.
Keyword ：
Graph neural network Graph neural network Heterogeneous information network Heterogeneous information network Meta-path combination Meta-path combination Semantic embedding Semantic embedding Semi-supervised classification Semi-supervised classification
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| GB/T 7714 | Chen, Yuhong , Chen, Fuhai , Wu, Zhihao et al. Heterogeneous Graph Embedding with Dual Edge Differentiation [J]. | NEURAL NETWORKS , 2025 , 183 . |
| MLA | Chen, Yuhong et al. "Heterogeneous Graph Embedding with Dual Edge Differentiation" . | NEURAL NETWORKS 183 (2025) . |
| APA | Chen, Yuhong , Chen, Fuhai , Wu, Zhihao , Chen, Zhaoliang , Cai, Zhiling , Tan, Yanchao et al. Heterogeneous Graph Embedding with Dual Edge Differentiation . | NEURAL NETWORKS , 2025 , 183 . |
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Multi-modal representation learning is recognized for its comprehensive interpretation across diverse modalities. Although existing approaches have yielded favorable results, they face challenges in high-order information preservation and out-of-sample data generalization. To tackle these issues, we propose a scalable multi-modal representation learning networks framework, which aims to learn optimal modality-specific projection matrices to project multi-modal features to a shared representation space. Specifically, weight guided modality-wise and row-sparsity driven feature-wise measures are considered to achieve adaptively hierarchical feature selection from the original data. Then, within the unified latent representation space, we employ hypergraph embedding to preserve the intricate high-order local geometric structures within the modality-specific high-dimensional spaces. Finally, we propose a proximal operator-inspired network architecture to resolve the optimization objectives, streamlining the process of feature auto-weighted selection and representation learning. The experimental results highlight the effectiveness and superiority of the proposed method, while online testing on out-of-sample data further demonstrates robust generalization. The code of the proposed method is publicly available at: https://github.com/ZihanFang11/SMMRL.
Keyword ：
Inductive models Inductive models Multi-modal learning Multi-modal learning Representation learning Representation learning Sparse regularization Sparse regularization
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| GB/T 7714 | Fang, Zihan , Zou, Ying , Lan, Shiyang et al. Scalable multi-modal representation learning networks [J]. | ARTIFICIAL INTELLIGENCE REVIEW , 2025 , 58 (7) . |
| MLA | Fang, Zihan et al. "Scalable multi-modal representation learning networks" . | ARTIFICIAL INTELLIGENCE REVIEW 58 . 7 (2025) . |
| APA | Fang, Zihan , Zou, Ying , Lan, Shiyang , Du, Shide , Tan, Yanchao , Wang, Shiping . Scalable multi-modal representation learning networks . | ARTIFICIAL INTELLIGENCE REVIEW , 2025 , 58 (7) . |
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With the increasing complexity of user-item interactions on the Internet, it is important to profile users and model their preferences in recommender systems. Traditional methods, including metric learning, rely on historical user-item interactions to model preferences but struggle in sparse data scenarios. While item tags offer valuable auxiliary information to enhance representations, their shared nature across items makes it challenging to effectively profile users with tags, which requires preserving user personalization through high-quality tag representations. Moreover, traditional optimization for user/item representations always takes place in Euclidean space, where the unconstrained nature of embedding norms tends to lean toward trivial solutions. This may bias the system towards common or popular preferences, thus suppressing the variety in tag-aware user profiles. To this end, we propose to profile users with tag-enhanced spherical metric learning for recommendation, named UTRec. Specifically, we propose an adaptive tag selection mechanism to ensure the quality of tag representations and learn tag-enhanced representations of users/items, thereby effectively profiling users. Additionally, we introduce a spherical optimization strategy for tag-enhanced recommendations to alleviate the limitations imposed by lazy learning and traditional optimization, ensuring the accuracy and diversity of user and item representations within the spherical space. Numerous experiments have been conducted on four real-world datasets, where our proposed tag-enhanced UTRec framework can bring consistent performance gains and achieve a 13.67% improvement regarding both Recall and NDCG metrics.
Keyword ：
Metric learning Metric learning Recommender system Recommender system Spherical optimization Spherical optimization Tag-enhanced Tag-enhanced User profiling User profiling
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| GB/T 7714 | Tan, Yanchao , Lv, Hang , Huang, Xinyi et al. Profiling users with tag-enhanced spherical metric learning for recommendation [J]. | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS , 2025 , 16 (9) : 5553-5567 . |
| MLA | Tan, Yanchao et al. "Profiling users with tag-enhanced spherical metric learning for recommendation" . | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 16 . 9 (2025) : 5553-5567 . |
| APA | Tan, Yanchao , Lv, Hang , Huang, Xinyi , Ma, Guofang , Chen, Chaochao . Profiling users with tag-enhanced spherical metric learning for recommendation . | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS , 2025 , 16 (9) , 5553-5567 . |
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With the rapid growth of activities on the web, large amounts of interaction data on multimedia platforms are easily accessible, including e-commerce, music sharing, and social media. By discovering various interests of users, recommender systems can improve user satisfaction without accessing overwhelming personal information. Compared to graph-based models, hypergraph-based collaborative filtering has the ability to model higher-order relations besides pair-wise relations among users and items, where the hypergraph structures are mainly obtained from specialized data or external knowledge. However, the above well-constructed hypergraph structures are often not readily available in every situation. To this end, we first propose a novel framework named HGRec, which can enhance recommendation via automatic hypergraph generation. By exploiting the clustering mechanism based on the user/item similarity, we group users and items without additional knowledge for hypergraph structure learning and design a cross-view recommendation module to alleviate the combinatorial gaps between the representations of the local ordinary graph and the global hypergraph. Furthermore, we devise a sparse optimization strategy to ensure the effectiveness of hypergraph structures, where a novel integration of the l( 2,1)-norm and optimal transport framework is designed for hypergraph generation. We term the model HGRec with sparse optimization strategy as HGRec++. Extensive experiments on public multi-domain datasets demonstrate the superiority brought by our HGRec++, which gains average 8.1% and 9.8% improvement over state-of-the-art baselines regarding Recall and NDCG metrics, respectively.
Keyword ：
graph convolutional network graph convolutional network hypergraph generation hypergraph generation Recommender systems Recommender systems sparse optimization sparse optimization
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| GB/T 7714 | Lin, Zhenghong , Yan, Qishan , Liu, Weiming et al. Automatic Hypergraph Generation for Enhancing Recommendation With Sparse Optimization [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 : 5680-5693 . |
| MLA | Lin, Zhenghong et al. "Automatic Hypergraph Generation for Enhancing Recommendation With Sparse Optimization" . | IEEE TRANSACTIONS ON MULTIMEDIA 26 (2024) : 5680-5693 . |
| APA | Lin, Zhenghong , Yan, Qishan , Liu, Weiming , Wang, Shiping , Wang, Menghan , Tan, Yanchao et al. Automatic Hypergraph Generation for Enhancing Recommendation With Sparse Optimization . | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 , 5680-5693 . |
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With the rapid development of Internet and Web techniques, Cross-Domain Recommendation (CDR) models have been widely explored for resolving the data-sparsity and cold-start problem. Meanwhile, most CDR models should utilize explicit domain-shareable information (e.g., overlapped users or items) for knowledge transfer across domains. However, this assumption may not be always satisfied since users and items are always non-overlapped in real practice. The performance of many previous works will be severely impaired when these domain-shareable information are not available. To address the aforementioned issues, we propose the Joint Preference Exploration and Dynamic Embedding Transportation model (JPEDET) in this paper which is a novel framework for solving the CDR problem when users and items are non-overlapped. JPEDET includes two main modules, i.e., joint preference exploration module and dynamic embedding transportation module. The joint preference exploration module aims to fuse rating and review information for modelling user preferences. The dynamic embedding transportation module is set to share knowledge via neural ordinary equations for dual transformation across domains. Moreover, we innovatively propose the dynamic transport flow equipped with linear interpolation guidance on barycentric Wasserstein path for achieving accurate and bidirectional transformation. Our empirical study on Amazon datasets demonstrates that JPEDET outperforms the state-of-the-art models under the CDR setting. Copyright © 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Keyword ：
Artificial intelligence Artificial intelligence Clock and data recovery circuits (CDR circuits) Clock and data recovery circuits (CDR circuits) Embeddings Embeddings Information dissemination Information dissemination Knowledge management Knowledge management Linear transformations Linear transformations User profile User profile
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| GB/T 7714 | Liu, Weiming , Chen, Chaochao , Liao, Xinting et al. Learning Accurate and Bidirectional Transformation via Dynamic Embedding Transportation for Cross-Domain Recommendation [C] . 2024 : 8815-8823 . |
| MLA | Liu, Weiming et al. "Learning Accurate and Bidirectional Transformation via Dynamic Embedding Transportation for Cross-Domain Recommendation" . (2024) : 8815-8823 . |
| APA | Liu, Weiming , Chen, Chaochao , Liao, Xinting , Hu, Mengling , Tan, Yanchao , Wang, Fan et al. Learning Accurate and Bidirectional Transformation via Dynamic Embedding Transportation for Cross-Domain Recommendation . (2024) : 8815-8823 . |
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Cross-Domain Recommendation (CDR) have become increasingly appealing by leveraging useful information to tackle the data sparsity problem across domains. Most of latest CDR models assume that domain-shareable user-item information (e.g., rating and review on overlapped users or items) are accessible across domains. However, these assumptions become impractical due to the strict data privacy protection policy. In this paper, we propose Reducing Item Discrepancy (RidCDR) model on solving Privacy-Preserving Cross-Domain Recommendation (PPCDR) problem. Specifically, we aim to enhance the model performance on both source and target domains without overlapped users and items while protecting the data privacy. We innovatively propose private-robust embedding alignment module in RidCDR for knowledge sharing across domains while avoiding negative transfer privately. Our empirical study on Amazon and Douban datasets demonstrates that RidCDR significantly outperforms the state-of-the-art models under the PPCDR without overlapped users and items. Copyright 2024 by the author(s)
Keyword ：
Differential privacy Differential privacy Privacy-preserving techniques Privacy-preserving techniques
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| GB/T 7714 | Liu, Weiming , Zheng, Xiaolin , Chen, Chaochao et al. Reducing Item Discrepancy via Differentially Private Robust Embedding Alignment for Privacy-Preserving Cross Domain Recommendation [C] . 2024 : 32455-32470 . |
| MLA | Liu, Weiming et al. "Reducing Item Discrepancy via Differentially Private Robust Embedding Alignment for Privacy-Preserving Cross Domain Recommendation" . (2024) : 32455-32470 . |
| APA | Liu, Weiming , Zheng, Xiaolin , Chen, Chaochao , Xu, Jiahe , Liao, Xinting , Wang, Fan et al. Reducing Item Discrepancy via Differentially Private Robust Embedding Alignment for Privacy-Preserving Cross Domain Recommendation . (2024) : 32455-32470 . |
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Recently, dual-target cross-domain recommendation (DTCDR) has been proposed to alleviate the data sparsity problem by sharing the common knowledge across domains simultaneously.However, existing methods often assume that personal data containing abundant identifiable information can be directly accessed, which results in a controversial privacy leakage problem of DTCDR.To this end, we introduce the P2DTR framework, a novel approach in DTCDR while protecting private user information.Specifically, we first design a novel inter-client knowledge extraction mechanism, which exploits the private set intersection algorithm and prototype-based federated learning to enable collaboratively modeling among multiple users and a server.Furthermore, to improve the recommendation performance based on the extracted common knowledge across domains, we proposed an intra-client enhanced recommendation, consisting of a constrained dominant set (CDS) propagation mechanism and dual-recommendation module.Extensive experiments on real-world datasets validate that our proposed P2DTR framework achieves superior utility under a privacy-preserving guarantee on both domains. © 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.
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| GB/T 7714 | Lin, Z. , Huang, W. , Zhang, H. et al. Enhancing Dual-Target Cross-Domain Recommendation with Federated Privacy-Preserving Learning [未知]. |
| MLA | Lin, Z. et al. "Enhancing Dual-Target Cross-Domain Recommendation with Federated Privacy-Preserving Learning" [未知]. |
| APA | Lin, Z. , Huang, W. , Zhang, H. , Xu, J. , Liu, W. , Liao, X. et al. Enhancing Dual-Target Cross-Domain Recommendation with Federated Privacy-Preserving Learning [未知]. |
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