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
  • 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
[1]GE S Y,WU C H,WU F Z,et al.Graph enhanced representation learning for news recommendation[C]//Proceedings of the Web Conference 2020.New York:ACM,2020:2863-2869.
[2]HU L M,XU S Y,LI C,et al.Graph neural news recommendation with unsupervised preference disentanglement[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.Stroudsburg:ACL,2020:4255-4264.
[3]WANG S Q,LI X X,SUN F Z,et al.Survey of research on personalized news recommendation techniques[J].Journal of Frontiers of Computer Science and Technology,2020,14(1):18-29.
[4]MENG X F,HUO H J,ZHANG X Y,et al.Survey of research on personalized news recommendation approaches[J].Journal of Frontiers of Computer Science and Technology,2023,17(12):2840-2860.
[5]TIAN X,DING Q,LIAO Z H,et al.Survey on deep learningbased news recommendation algorithm[J].Journal of Frontiers of Computer Science and Technology,2021,15(6):971-998.
[6]MOREIRA G,JANNACH D,CUNHA A.Contextual hybridsession-based news recommendation with recurrent neural networks[J].IEEE Access,2019,7:169185-169203.
[7]WU C H,WU F Z,AN M X,et al.Neural news recommendation with attentive multi-view learning[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence.Palo Alto:AAAI Press,2019:3863-3869.
[8]ZHANG Q,JIA Q L,WANG C Y,et al.AMM:Attentive multifield matching for news recommendation[C]//The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval.New York:ACM,2021:1588-1592.
[9]MAO Z M,LI J,WANG H R,et al.DIGAT:Modeling news re-commendation with dual-graph interaction[C]//Findings of the Association for Computational Linguistics.Stroudsburg:ACL,2022:6595-6607.
[10]HU L M,LI C,SHI C,et al.Graph neural news recommendation with long-term and short-term interest modeling[J].Information Processing and Management,2020,57(2):102142.
[11]SCHÖLKOPF B,PLATT J,HOFMANN T.Learning with Hypergraphs:Clustering,Classification,and Embedding[C]//Advances in Neural Information Processing Systems 19:Procee-dings of the 2006 Conference.Cambridge:MIT Press,2007:1601-1608.
[12]HU B D,WANG X E,WANG X Y,et al.Survey on hypergraphlearning:algorithm classification and application analysis[J].Journal of Software,2022,33(2):498-523.
[13]FENG Y F,YOU H X,ZHANG Z Z,et al.Hypergraph neural networks[C]//Proceedings of the Thirty-Third AAAI Confe-rence on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence(AAAI’19/ IAAI’19/ EAAI’19).Palo Alto:AAAI Press,2019:3558-3565.
[14]YADATI N,NIMISHAKAVI M,YADAV P,et al.Hyper-GCN:a new method of training graph convolutional networks on hypergraphs[C]//Proceedings of the 33rd International Confe-rence on Neural Information Processing Systems.New York:Curran Associates Inc.,2019:1511-1522.
[15]WANG J L,DING K Z,HONG L J,et al.Next-item recommendation with sequential hyper-graphs[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR’20).New York:ACM,2020:1101-1110.
[16]XIA X,YIN H Z,YU J L,et al.Self-supervised hypergraph con-volutional networks for session-based recommendation[C]//Proceedings of the AAAI Conference on Artificial Intelligence,35.Palo Alto:AAAI Press,2021:4503-4511.
[17]WANG J L,DING K Z,ZHU Z W,et al.Session-based recommendation with hypergraph attention networks[C]//Procee-dings of the 2021 SIAM International Conference on Data Mi-ning(SDM).Philadelphia:SIAM,2021:82-90.
[18]YU J L,YIN H Z,LI J D,et al.Self-supervised multi-channel hypergraph convolutional network for social recommendation[C]//Proceedings of the Web Conference 2021.New York:ACM,2021:413-424.
[19]WANG Z H.English news text recommendation method based on hypergraph random walk label expansion[J].Computational Intelligence and Neuroscience,2022,2022.
[20]VASWANI A,SHAZEER N,PARMAR N,et al.Attention isall you need[C]//Proceedings of the 31st International Confe-rence on Neural Information Processing Systems.New York:Curran Associates Inc.,2017:6000-6010.
[21]LIU W X,ZHANG Z Z,WANG B.Dual-view hypergraph attention network for news recommendation [J].Engineering Applications of Artificial Intelligence,2024,133(C):108256.
[22]FENG Y F,LYU F Y,SHEN W C,et al.Deep session interest network for click-through rate prediction[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence(IJCAI’19).Palo Alto:AAAI Press,2019:2301-2307.
[23]WU C H,WU F Z,GE S Y,et al.Neural news recommendation with multi-head self-attention[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing(EMNLP-IJCNLP).Stroudsburg:ACL,2019:6389-6394.
[24]WU F Z,QIAO Y,CHEN J H,et al.MIND:A large-scale dataset for news recommendation[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.Stroudsburg:ACL,2020:3597-3606.
[25]PENNINGTON J,SOCHER R,MANNING C.GloVe:Globalvectors for word representation[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Proces-sing.Stroudsburg:ACL,2014:1532-1543.
[26]KINGMA D,BA J.Adam:a method for stochastic optimization[C]//Proceedings of the International Conference on Learning Representations.Washington:ICLR,2015:1412-1427.
[27]RENDLE S.Factorization machines with libFM[J].ACMTransactions on Intelligent Systems and Technology,2012,3(3):1-22.
[28]GUO H F,TANG R M,YE Y M,et al.DeepFM:A factorization-machine based neural network for CTR prediction[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence.Palo Alto:AAAI Press,2017:1725-1731.
[29]AN M X,WU F Z,WU C H,et al.Neural news recommendation with long- and short-term user representations[C]//Procee-dings of the 57th Annual Meeting of the Association for Computational Linguistics.Stroudsburg:ACL,2019:336-345.
[30]YANG B M,LIU D R,SUZUMURA T,et al.Going beyond local:Global graph-enhanced personalized[C]//Proceedings of the 17th ACM Conference on Recommender Systems.New York:ACM,2023:24-34.
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