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A Three-Layered Mutually Reinforced Model for Personalized Citation Recommendation.

Xiaoyan Cai, Junwei Han, Wenjie Li

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    Summary

    This study introduces a novel citation recommendation system using a three-layered graph for personalized recommendations. The approach enhances accuracy and efficiency by employing interactive clustering, outperforming non-personalized methods.

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    Area of Science:

    • Information Science
    • Computer Science
    • Bibliometrics

    Background:

    • The rapid growth of scientific literature necessitates efficient methods for discovering relevant reference papers.
    • Citation recommendation is a crucial technique to address this challenge in academic research.

    Purpose of the Study:

    • To develop an effective and computationally efficient citation recommendation approach.
    • To enable personalized reference suggestions for researchers based on their specific needs.

    Main Methods:

    • A three-layered graph model representing papers, authors, and venues was utilized.
    • Mutual reinforcement was applied within the graph structure for enhanced recommendations.
    • A three-layered interactive clustering approach was developed to reduce computational complexity.

    Main Results:

    • The proposed citation recommendation approach demonstrated performance comparable to state-of-the-art methods on benchmark datasets (ACL, DBLP, CiteSeer ML).
    • Personalized recommendations generated using the clustering method proved more effective than non-personalized ones.

    Conclusions:

    • The interactive clustering on a three-layered graph offers an efficient solution for personalized citation recommendation.
    • This method significantly improves the accuracy and relevance of suggested references for researchers.