结合超图学习的多注意力机制新闻推荐方法

doi:10.11896/jsjkx.250200067

摘要/Abstract

摘要： 在个性化新闻推荐中,图结构常被用来建立用户与新闻之间的交互关系。然而,普通图结构大多忽略了被点击新闻之间的高阶关联信息。此外,现有方法大多仅使用单一向量学习用户兴趣表示与候选新闻表示,导致建模不充分。针对上述问题,提出了结合超图学习的多注意力机制新闻推荐模型。首先,构建候选新闻超图,通过超图注意力网络的学习捕获候选新闻与其语义相似新闻的高阶相关性,丰富候选新闻语义;然后,构建新闻-主题超图用于建模用户兴趣,采用包含多种注意力机制的神经网络架构挖掘深层的用户细粒度兴趣特征;最后,通过引入激活单元,结合候选新闻特征进一步提取用户兴趣,从而提高推荐准确性。在MIND-small和MIND-large数据集上进行的大量实验,验证了所提方法的有效性。

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

中图分类号:

TP391

孟祥福, 王琬淳, 张雨萌, 樊文懿. 结合超图学习的多注意力机制新闻推荐方法[J]. 计算机科学, 2025, 52(11A): 250200067-7. https://doi.org/10.11896/jsjkx.250200067

MENG Xiangfu, WANG Wanchun, ZHANG Yumeng, FAN Wenyi. Multiple Attention Mechanism News Recommendation Approach with Hypergraph Learning[J]. Computer Science, 2025, 52(11A): 250200067-7. https://doi.org/10.11896/jsjkx.250200067

参考文献

[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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