[1]周俊,张志强,曹月恬,等.地理位置和时间感知的表示学习框架[J].智能系统学报,2021,16(5):909-917.[doi:10.11992/tis.202104011]
 ZHOU Jun,ZHANG Zhiqiang,CAO Yuetian,et al.A geography and time aware representation learning framework[J].CAAI Transactions on Intelligent Systems,2021,16(5):909-917.[doi:10.11992/tis.202104011]
点击复制

地理位置和时间感知的表示学习框架

参考文献/References:
[1] DU Lun, WANG Yun, SONG Guojie, et al. Dynamic network embedding: an extended approach for skip-gram based network embedding[C]//Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18). Stockholm, Sweden, 2018: 2086-2092.
[2] SINGER U, GUY I, RADINSKY K. Node embedding over temporal graphs[C]//Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. Macao, China, 2019: 4605-4612.
[3] GUO Shengnan, LIN Youfang, FENG Ning, et al. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting[J]. Proceedings of the AAAI conference on artificial intelligence, 2019, 33(1): 922-929.
[4] YANG Shuo, ZHANG Zhiqiang, ZHOU Jun, et al. Financial risk analysis for SMEs with graph-based supply chain mining[C]//Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20). Yokohama, Japan, 2020: 4661-4667.
[5] XU Da, RUAN Chuanwei, KORPEOGLU E, et al. Inductive representation learning on temporal graphs[C]//2020 International Conference on Learning Representations. Addis Ababa, Ethiopia, 2020: 1-19.
[6] HUANG Hong, FANG Zixuan, WANG Xiao, et al. Motif-preserving temporal network embedding[C]//Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20). Yokohama, Japan, 2020: 1237-1243.
[7] MINH H Q, NIYOGI P, YAO Yuan. Mercer’s theorem, feature maps, and smoothing[C]//Proceedings of the 19th Annual Conference on Learning Theory (COLT 2006). Pittsburgh, USA, 2006: 154-168.
[8] LIAN Defu, WU Yongji, GE Yong, et al. Geography-aware sequential location recommendation[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Virtual Event, USA, 2020: 2009-2019.
[9] OMI T, UEDA N, AIHARA K. Fully neural network based model for general temporal point processes[C]//Proceedings of the 33st International Conference on Neural Information Processing Systems. Vancouver, Canada, 2019: 2120-2129.
[10] ZHU Yu, LI Hao, LIAO Yikang, et al. What to do next: modeling user behaviors by time-LSTM[C]//Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17). Melbourne, Australia, 2017: 3602-3608.
[11] KUMAR S, ZHANG Xikun, LESKOVEC J. Predicting dynamic embedding trajectory in temporal interaction networks[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Anchorage, USA, 2019: 1269-1278.
[12] HAMILTON W L, YING R, LESKOVEC J. Inductive representation learning on large graphs[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, CA, USA, 2017: 1025-1035.
[13] VELI?KOVI? P, CUCURULL G, CASANOVA A, et al. Graph attention networks[C]//The 6th International Conference on Learning Representations. Vancouver, BC, Canada, 2018: 1-12.
[14] NGUYEN G H, LEE J B, ROSSI R A, et al. Continuous-time dynamic network embeddings[C]//Companion Proceedings of the the Web Conference 2018. Lyon, France, 2018: 969-976.
[15] LU Yuanfu, WANG Xiao, SHI Chuan, et al. Temporal network embedding with micro-and macro-dynamics[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management. Beijing, China, 2019: 469-478.
[16] SEO Y, DEFFERRARD M, VANDERGHEYNST P, et al. Structured sequence modeling with graph convolutional recurrent networks[C]//Processing of the 25th International Conference (ICONIP 2018). Siem Reap, Cambodia, 2018: 362-373.
[17] GUO Huifeng, TANG Ruiming, YE Yunming, et al. DeepFM: a factorization-machine based neural network for CTR prediction[C]//Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17). Melbourne, Australia, 2017: 1725-1731.
[18] HIDASI B, KARATZOGLOU A, BALTRUNAS L, et al. Session-based recommendations with recurrent neural networks[C]//International Conference on Learning Representations. San Juan, Puerto Rico, 2016: 1-10.
[19] ZHOU Guorui, ZHU Xiaoqiang, SONG Chenru, et al. Deep interest network for click-through rate prediction[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. London, United Kingdom, 2018: 1059-1068.
[20] KANG Wangcheng, MCAULEY J. Self-attentive sequential recommendation[C]//2018 IEEE International Conference on Data Mining (ICDM). Singapore, Singapore, 2018: 197-206.
[21] ZHOU Guorui, MOU Na, FAN Ying, et al. Deep interest evolution network for click-through rate prediction[J]. Proceedings of the AAAI conference on artificial intelligence, 2019, 33(1): 5941–5948.
[22] MA Jiaqi, ZHAO Zhe, YI Xinyang, et al. Modeling task relationships in multi-task learning with multi-gate mixture-of-experts[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. London, United Kingdom, 2018: 1930-1939.
[23] HU Binbin, ZHANG Zhiqiang, ZHOU Jun, et al. Loan default analysis with multiplex graph learning[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2020: 2525-2532.
[24] WANG D, ZHANG Z, ZHOU J, et al. Temporal-aware graph neural network for credit risk prediction[C]//Proceedings of the 2021 SIAM International Conference on Data Mining (SDM). 2021: 702-710.
[25] HU B, ZHANG Z, SHI C, et al. Cash-out user detection based on attributed heterogeneous information network with a hierarchical attention mechanism[C]//Proceedings of the AAAI Conference on Artificial Intelligence. Hawaii, USA, 2019: 946-953.

备注/Memo

收稿日期:2021-04-07。
基金项目:国家重点研发计划项目(2018YFB1403001)
作者简介:周俊,蚂蚁集团研究员,主要研究方向为机器学习,获得了第十届吴文俊人工智能科技进步奖一等奖(第一完成人)。发表学术论文40余篇;张志强,蚂蚁集团高级算法专家,主要研究方向为图机器学习,发表学术论文20余篇;郑小林,教授,博士生导师,主要研究方向为人工智能、智能电商、金融智能、服务计算。主持国家自然科学基金重点项目等多项。发表学术论文50余篇
通讯作者:郑小林.E-mail:xlzheng@zju.edu.cn

更新日期/Last Update: 1900-01-01
Copyright @ 《 智能系统学报》 编辑部
地址:(150001)黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话:0451- 82534001、82518134