Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (11A): 250200067-7.doi: 10.11896/jsjkx.250200067

• Big Data & Data Science • Previous Articles     Next Articles

Multiple Attention Mechanism News Recommendation Approach with Hypergraph Learning

MENG Xiangfu, WANG Wanchun, ZHANG Yumeng, FAN Wenyi   

  1. School of Electronic and Information Engineering,Liaoning Technical University,Huludao,Liaoning 125105,China
  • Online:2025-11-15 Published:2025-11-10
  • About author:MENG Xiangfu,born in 1981,Ph.D,professor,Ph.D supervisor.His main research interests include spatio-temporal big data analysis,recommendation system and artificial intelligence.
    WANG Wanchun,born in 2000,master.Her main research interests include re-commendation system and artificial intelligence.
  • Supported by:
    National Natural Science Foundation of China(61772249).

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Abstract: In personalized news recommendation,graph structures are often utilized to establish interaction relationships between users and news,however conventional graph structures mostly overlook the high-order association information among clicked news items.Furthermore,existing methods typically rely on a single vector to learn user interest representations and candidate news representations,leading to inadequate modeling.To address these issues,a multiple attention mechanism news recommendation model approach with hypergraph learning is proposed.Firstly,a candidate news hypergraph is constructed,leveraging a hypergraph attention network to capture high-order correlations between candidate news and their semantically similar news,thereby enriching the semantics of candidate news.Secondly,a news-topic hypergraph is built to model user interests,employing a neural network architecture with multiple attention mechanisms to explore deep,fine-grained user interest features.Lastly,an activation unit is introduced to further extract user interests from candidate news,enhancing recommendation accuracy.The experiments on the MIND-small and MIND-large datasets confirm the effectiveness of the proposed approach.

Key words: Recommendation system, Personalized news recommendation, Semantic augment, User interest, Hypergraph learning, Hypergraph attention network, Attention mechanism

CLC Number: 

  • TP391
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