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Related Experiment Video

Updated: Nov 2, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Knowledge-Guided Article Embedding Refinement for Session-Based News Recommendation.

Heng-Shiou Sheu, Zhixuan Chu, Daiqing Qi

    IEEE Transactions on Neural Networks and Learning Systems
    |June 9, 2021
    PubMed
    Summary

    This study introduces a context-aware graph embedding (CAGE) approach for session-based news recommendation. CAGE enhances news article representations using knowledge graphs and graph neural networks, improving recommendation accuracy.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Information Retrieval

    Background:

    • Personalized news recommendation faces challenges due to limited user interaction data.
    • Session-based recommendation models often fail to leverage semantic information among news articles.

    Purpose of the Study:

    • To propose a novel context-aware graph embedding (CAGE) approach for session-based news recommendation.
    • To enhance news article representations by incorporating external knowledge graphs and graph neural networks.

    Main Methods:

    • Utilized external knowledge graphs to enrich semantic representations of news articles.
    • Employed graph neural networks to refine article embeddings.
    • Incorporated attention neural networks to model user preferences based on session similarity.

    Main Results:

    • The CAGE approach demonstrated superior performance compared to existing baseline methods on multiple benchmark datasets.
    • Improved accuracy in predicting the next news article within a user session.

    Conclusions:

    • CAGE effectively addresses limitations in current session-based news recommendation methods.
    • Integrating external knowledge and advanced neural network architectures enhances recommendation quality.