基于Transformer和知识图谱的新闻推荐新方法

计算机技术、通信工程    基于Transformer和知识图谱的新闻推荐新方法1. 天津财经大学 统计学院，天津 300222
2. 中央财经大学 信息学院，北京 100081New method for news recommendation based on Transformer and knowledge graphLi-zhou FENG1( ),Yang YANG1,You-wei WANG2,*( ),Gui-jun YANG11. School of Statistics, Tianjin University of Finance and Economics, Tianjin 300222, China
2. School of Information, Central University of Finance and Economics, Beijing 100081, China 摘要 图/表 参考文献 相关文章 Metrics引用本文:

凤丽洲,杨阳,王友卫,杨贵军. 基于Transformer和知识图谱的新闻推荐新方法[J]. 浙江大学学报(工学版), 2023, 57(1): 133-143.

Li-zhou FENG,Yang YANG,You-wei WANG,Gui-jun YANG. New method for news recommendation based on Transformer and knowledge graph. Journal of ZheJiang University (Engineering Science), 2023, 57(1): 133-143.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2023.01.014   或   https://www.zjujournals.com/eng/CN/Y2023/V57/I1/133

1 LI L, CHU W, LANGFORD J, et al. A contextual-bandit approach to personalized news article recommendation [C]// Proceedings of the 19th International Conference on World Wide Web. Raleigh: ACM, 2010: 661-670.2 KOREN Y, BELL R, VOLINSKY C Matrix factorization techniques for recommender systems[J]. Computer, 2009, 42 (8): 30- 37
doi: 10.1109/MC.2009.2633 SUN Z, GUO Q, YANG J, et al Research commentary on recommendations with side information: a survey and research directions[J]. Electronic Commerce Research and Applications, 2019, 37 (1): 1- 304 WANG H, ZHANG F, XIE X, et al. DKN: deep knowledge-aware network for news recommendation [C]// Proceedings of the 2018 World Wide Web Conference. Lyon: ACM, 2018: 1835-1844.5 WANG H, ZHANG F, ZHAO M, et al. Multi-task feature learning for knowledge graph enhanced recommendation [C]// Proceedings of the 2019 World Wide Web Conference. San Francisco: ACM, 2019: 2000-2010.6 XIAN Y, FU Z, MUTHUKRISHNAN S, et al. Reinforcement knowledge graph reasoning for explainable recommendation [C]// Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. Paris: ACM, 2019: 285-294.7 宁泽飞, 孙静宇, 王欣娟 基于知识图谱和标签感知的推荐算法[J]. 计算机科学, 2021, 48 (11): 192- 198
NING Ze-fei, SUN Jing-yu, WANG Xin-juan Recommendation algorithm based on knowledge graph and tag-aware[J]. Computer Science, 2021, 48 (11): 192- 198
doi: 10.11896/jsjkx.2010000858 WANG H, ZHAO M, XIE X, et al. Knowledge graph convolutional networks for recommender systems [C]// Proceedings of the 2019 World Wide Web Conference. San Francisco: ACM, 2019: 3307-3313.9 WANG H, ZHANG F, WANG J, et al. Ripplenet: propagating user preferences on the knowledge graph for recommender systems [C]// Proceedings of the 27th ACM International Conference on Information and Knowledge Management. Torino: ACM, 2018: 417-426.10 刘羽茜, 刘玉奇, 张宗霖, 等 注入注意力机制的深度特征融合新闻推荐模型[J]. 计算机应用, 2022, 42 (2): 426- 432
LIU Yu-xi, LIU Yu-qi, ZHANG Zong-lin, et al News recommendation model with deep feature fusion injecting attention mechanism[J]. Computer Applications, 2022, 42 (2): 426- 43211 CHEN Q, ZHAO H, LI W, et al. Behavior sequence transformer for e-commerce recommendation in Alibaba [C]// Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data. Anchorage: ACM, 2019: 1-4.12 TANG J, WANG K. Personalized top-n sequential recommendation via convolutional sequence embedding [C]// Proceedings of the 11th ACM International Conference on Web Search and Data Mining. Marina Del Rey: ACM, 2018: 565-573.13 冯永, 张备, 强保华, 等 MN-HDRM: 长短兴趣多神经网络混合动态推荐模型[J]. 计算机学报, 2019, 42 (1): 16- 28
FENG Yong, ZHANG Bei, QIANG Bao-hua, et al MN-HDRM: a novel hybrid dynamic recommendation model based on long-short-term interests multiple neural networks[J]. Journal of Computer Science, 2019, 42 (1): 16- 2814 VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need [C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach: MIT Press, 2017: 6000-6010.15 BANSAL T, DAS M, BHATTACHARYYA C. Content driven user profiling for comment-worthy recommendations of news and blog articles [C]// Proceedings of the 9th ACM Conference on Recommender Systems. Vienna: ACM, 2015: 195-202.16 KUMAR V, KHATTAR D, GUPTA S, et al. Deep neural architecture for news recommendation [C]// Proceedings of the 2017 Conference and Labs of the Evaluation Forum. Dublin: [s. n. ], 2017: 1-19.17 ZHANG Q, LI J, JIA Q, et al. UNBERT: user-news matching BERT for news recommendation [C]// Proceedings of the 30th International Joint Conference on Artificial Intelligence. Montreal: Morgan Kaufmann, 2021: 3356-3362.18 WU C, WU F, QI T, et al. Feedrec: news feed recommendation with various user feedbacks [C]// Proceedings of the ACM Web Conference. Lyon: ACM, 2022: 2088-2097.19 QI T, WU F, WU C, et al. Personalized news recommendation with knowledge-aware interactive matching [C]// Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. Canada: ACM, 2021: 61-70.20 LIU D, LIAN J, LIU Z, et al. Reinforced anchor knowledge graph generation for news recommendation reasoning [C]// Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Singapore: ACM, 2021: 1055-1065.21 VRANDECIC D, KROTZSCH M Wikidata: a free collaborative knowledgebase[J]. Communications of the ACM, 2014, 57 (10): 78- 85
doi: 10.1145/262948922 XU B, XU Y, LIANG J, et al. CN-DBpedia: a never-ending Chinese knowledge extraction system [C]// Proceedings of the 30th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Arras: Springer, 2017: 428-438.23 SUCHANEK F M, KASNECI G, WEIKUM G. Yago: a core of semantic knowledge [C]// Proceedings of the 16th International Conference on World Wide Web. Banff: ACM, 2007: 697-706.24 WU C, WU F, GE S, 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. Hong Kong: ACL, 2019: 6389-6394.25 WU F, 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: 3597-3606.26 MIKOLOV T, CHEN K, CORRADO G, et al. Efficient estimation of word representations in vector space [C]// Proceedings of the 1st International Conference on Learning Representations. Scottsdale: [s. n. ], 2013: 1-12.27 HU L, XU S, 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.ViewedFull text


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