[1]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]
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A geography and time aware representation learning framework

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 5 Page number: 909-917 Column: 吴文俊人工智能科技进步奖一等奖 Public date: 2021-09-05
Title:
A geography and time aware representation learning framework
Author(s):
ZHOU Jun1; 2;  ZHANG Zhiqiang2;  CAO Yuetian2;  ZHENG Xiaoling1
1. College of Computer Science and Technology, Zhejiang University, Hangzhou 310007, China;
2. Ant Group, Hangzhou 310013, China
Keywords:
semantic-space-time mode; time modelling; spatial modelling; attention mechanism; graph learning; graph neural network; user behavior modeling; user behavior representation
CLC:
TP183
DOI:
10.11992/tis.202104011
Abstract:
The existing geography and time aware representation learning framework fails to give a unified solution to three problems of “When”, “Where” and “What” in real-world scenarios with strong geography and time aware semantics, and the existing research schemes for time and spatial modeling also have certain defects and thus cannot achieve optimal performance in complex actual scenarios. To fill in these gaps, this paper proposes a unified user representation framework—GTRL (geography and time aware representation learning), which is capable of jointly modeling users’ historical behaviors in both time and spatial dimensions. In terms of time modeling, GTRL adopts a functional time encoding and a continuous time and context aware graph attention network to flexibly exploit high-order and structured temporal information from dynamic user behaviour graphs. In terms of spatial modeling, GTRL uses a hierarchical geography encoding and a deep historical trajectory modelling module to effectively depict users’ preference for geographical location. Moreover, a unified optimization strategy is carefully designed to carry out model learning on three tasks of interaction prediction task, interaction time stamp prediction task and interaction geography prediction. At last, extensive experiments on public and industrial data sets are carried out, demonstrating the advantages of GTUL over the academic baseline model and its effectiveness in actual business scenarios, respectively.
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.
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