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第1期 吴国栋,等:图神经网络推荐研究进展 ·23· LIU Jianxun,SHI Min,ZHOU Dong,et al.Topic model tion:a recurrent model with spatial and temporal contexts[Cl based tag recommendation method for Mashups[J]. Proceedings of the Thirtieth AAAI Conference on Artifi- Chinese journal of computers,2017,40(2):520-534. cial Intelligence.Arizona.USA2016:194-200. [21]曹俊豪,李泽河,江龙,等.一种融合协同过滤和用户属 [29]XU.KEYULU,et al.How powerful are graph neural net- 性过滤的混合推荐算法.电子设计工程,2018.26(9): works [J].arXiv:1810.00826.2018. 60-63 [30]SONG Weiping,XIAO Zhiping,WANG Yifan,et al.Ses- CAO Junhao,LI Zehe,JIANG Long,et al.A hybrid re- sion-based social recommendation via dynamic graph at- commendation algorithm based on collaborative filtering tention networks[C]//Proceedings of the Twelfth ACM In- and user attribute filtering[J].Electronic design engineer- ternational Conference on Web Search and Data Mining ing,2018,26(9):60-63 New York,United States,2019:555-563. 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[27]ZHENG Lei.NOROOZI V.YU P S.Joint deep modeling SHEN Jiquan,WANG Lei,HOU Zhanwei,et al.Attrac- of users and items using reviews for recommendation[Cl// tions recommendation algorithm based on situational con- Proceedings of the Tenth ACM International Conference text and trust relationship[J].Application research of on Web Search and Data Mining.New York,USA,2017: computers,2018,35(12):3640-3643. 425-434 [39]李林峰,刘真,魏港明,等.基于共享知识模型的跨领域 [28]LIU Q,WU S,WANG L,et al.Predicting the next loca- 推荐算法[J.电子学报,2018,46(8:1947-1953LIU Jianxun, SHI Min, ZHOU Dong, et al. Topic model based tag recommendation method for Mashups[J]. Chinese journal of computers, 2017, 40(2): 520–534. 曹俊豪, 李泽河, 江龙, 等. 一种融合协同过滤和用户属 性过滤的混合推荐算法 [J]. 电子设计工程, 2018, 26(9): 60–63. CAO Junhao, LI Zehe, JIANG Long, et al. A hybrid re￾commendation algorithm based on collaborative filtering and user attribute filtering[J]. Electronic design engineer￾ing, 2018, 26(9): 60–63. [21] 张双庆. 一种基于用户的协同过滤推荐算法 [J]. 电脑知 识与技术, 2019, 15(1): 19–21. ZHANG Shuangqing. User-based collaborative filtering recommendation algorithm[J]. Computer knowledge and technology, 2019, 15(1): 19–21. [22] 邓园园, 吴美香, 潘家辉. 基于物品的改进协同过滤算 法及应用 [J]. 计算机系统应用, 2019, 28(1): 182–187. DENG Yuanyuan, WU Meixiang, PAN Jiahui. Improved item-based collaborative filtering algorithm and its applic￾ation[J]. Computer systems & applications, 2019, 28(1): 182–187. [23] 高玉凯, 王新华, 郭磊, 等. 一种基于协同矩阵分解的用 户冷启动推荐算法 [J]. 计算机研究与发展, 2017, 54(8): 1813–1823. GAO Yukai, WANG Xinhua, GUO Lei, et al. Learning to recommend with collaborative matrix factorization for new users[J]. Journal of computer research and develop￾ment, 2017, 54(8): 1813–1823. [24] 王伟, 陈伟, 祝效国, 等. 众筹项目的个性化推荐: 面向 稀疏数据的二分图模型 [J]. 系统工程理论与实践, 2017, 37(4): 1011–1023. WANG Wei, CHEN Wei, ZHU K, et al. Personalized re￾commendation of crowd-funding campaigns: a bipartite graph approach for sparse data[J]. Systems engineering– theory & practice, 2017, 37(4): 1011–1023. [25] ELKAHKY A M, SONG Yang, HE Xiaodong. A multi￾view deep learning approach for cross domain user mod￾eling in recommendation systems[C]//Proceedings of the 24th International Conference on World Wide Web. Florence, Italy, 2015: 278−288. [26] ZHENG Lei, NOROOZI V, YU P S. Joint deep modeling of users and items using reviews for recommendation[C]// Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. New York, USA, 2017: 425−434. [27] [28] LIU Q, WU S, WANG L, et al. Predicting the next loca￾tion: a recurrent model with spatial and temporal contexts[C]// Proceedings of the Thirtieth AAAI Conference on Artifi￾cial Intelligence. Arizona, USA ,2016: 194−200. XU,KEYULU,et al.How powerful are graph neural net￾works [J]. arXiv:1810.00826, 2018. [29] SONG Weiping, XIAO Zhiping, WANG Yifan, et al. Ses￾sion-based social recommendation via dynamic graph at￾tention networks[C]//Proceedings of the Twelfth ACM In￾ternational Conference on Web Search and Data Mining. New York, United States, 2019: 555−563. [30] FAN Wenqi, MA Yao, LI Qing, et al. Graph neural net￾works for social recommendation[C]//The World Wide Web Conference. New York, USA, 2019: 417−426. [31] YING R, HE Ruining, CHEN Kaifeng, et al. Graph con￾volutional neural networks for web-scale recommender systems[C]//Proceedings of the 24th ACM SIGKDD In￾ternational Conference on Knowledge Discovery & Data Mining. New York, USA, 2018: 974−983. [32] CUI Zeyu, LI Zekun, WU Shu, et al. Dressing as a whole: outfit compatibility learning based on node-wise graph neural networks[C]//The World Wide Web Conference. New York, USA, 2019: 307−317. [33] WANG X, HE X, WANG M, et al. Neural graph collab￾orative filtering[J]. arXiv:1905.08108, 2019. [34] WANG Hongwei, ZHAO Miao, XIE Xing, et al. Know￾ledge graph convolutional networks for recommender sys￾tems[C]//The World Wide Web Conference. San Fran￾cisco, USA, 2019: 3307−3313. [35] MAO C, YAO L, LUO Y. MedGCN: Graph convolution￾al networks for multiple medical tasks[J]. arXiv: 1904.00326, 2019. [36] 刘云, 王颖, 亓国涛, 等. 时间上下文的协同过滤 Top￾N 推荐算法 [J]. 计算机技术与发展, 2017, 27(7): 79–82. LIU Yun, WANG Ying, QI Guotao, et al. Collaborative filtering top-N recommendation algorithm based on time context[J]. Computer technology and development, 2017, 27(7): 79–82. [37] 沈记全, 王磊, 侯占伟, 等. 基于情景上下文与信任关系 的旅游景点推荐算法 [J]. 计算机应用研究, 2018, 35(12): 3640–3643. SHEN Jiquan, WANG Lei, HOU Zhanwei, et al. Attrac￾tions recommendation algorithm based on situational con￾text and trust relationship[J]. Application research of computers, 2018, 35(12): 3640–3643. [38] 李林峰, 刘真, 魏港明, 等. 基于共享知识模型的跨领域 推荐算法 [J]. 电子学报, 2018, 46(8): 1947–1953. [39] 第 1 期 吴国栋,等:图神经网络推荐研究进展 ·23·
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