{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T23:47:02Z","timestamp":1768520822043,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":63,"publisher":"ACM","funder":[{"name":"National Natural Science Foundation of China","award":["62272098"],"award-info":[{"award-number":["62272098"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,8,3]]},"DOI":"10.1145\/3711896.3737186","type":"proceedings-article","created":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T21:05:41Z","timestamp":1754255141000},"page":"5071-5082","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["A Transferable Spatio-temporal Learning Framework for Cross-city Logistics Demand Prediction"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6550-5160","authenticated-orcid":false,"given":"Kaiwen","family":"Xia","sequence":"first","affiliation":[{"name":"Southeast University, Nanjing, China and JD Logistics, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3511-5559","authenticated-orcid":false,"given":"Li","family":"Lin","sequence":"additional","affiliation":[{"name":"Southeast University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8366-0453","authenticated-orcid":false,"given":"Xinrui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Southeast University, Nanjing, China and JD Logistics, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9783-6389","authenticated-orcid":false,"given":"Haotian","family":"Wang","sequence":"additional","affiliation":[{"name":"JD Logistics, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3609-2205","authenticated-orcid":false,"given":"Shuai","family":"Wang","sequence":"additional","affiliation":[{"name":"Southeast University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6062-2619","authenticated-orcid":false,"given":"Tian","family":"He","sequence":"additional","affiliation":[{"name":"JD Logistics, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"2023. Business-to-Business E-commerce Market Size Share Trends Analysis Report By Product Category. Grand View Research."},{"key":"e_1_3_2_2_2_1","volume-title":"Time series analysis: forecasting and control","author":"Box George EP","unstructured":"George EP Box, Gwilym M Jenkins, Gregory C Reinsel, and Greta M Ljung. 2015. Time series analysis: forecasting and control. John Wiley & Sons."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467132"},{"key":"e_1_3_2_2_5_1","volume-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555","author":"Chung Junyoung","year":"2014","unstructured":"Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671961"},{"key":"e_1_3_2_2_7_1","volume-title":"Exploring monte carlo methods","author":"Dunn William L","unstructured":"William L Dunn and J Kenneth Shultis. 2022. Exploring monte carlo methods. Elsevier."},{"key":"e_1_3_2_2_8_1","volume-title":"et al","author":"Fang Ziquan","year":"2022","unstructured":"Ziquan Fang, Dongen Wu, Lu Pan, et al . 2022. When transfer learning meets cross-city urban flow prediction: spatio-temporal adaptation matters. IJCAI'22 (2022), 2030-2036."},{"key":"e_1_3_2_2_9_1","volume-title":"International conference on machine learning. PMLR, 1126-1135","author":"Finn Chelsea","year":"2017","unstructured":"Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In International conference on machine learning. PMLR, 1126-1135."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-13560-1_76"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05488-0"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i1.16088"},{"key":"e_1_3_2_2_13_1","volume-title":"Long short-term memory. Neural computation 9, 8","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735-1780."},{"key":"e_1_3_2_2_14_1","volume-title":"Regional logistics demand forecasting: a BP neural network approach. Complex & Intelligent Systems","author":"Huang Lijuan","year":"2021","unstructured":"Lijuan Huang, Guojie Xie, Wende Zhao, Yan Gu, and Yi Huang. 2021. Regional logistics demand forecasting: a BP neural network approach. Complex & Intelligent Systems (2021), 1-16."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599853"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/367"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599824"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539250"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599529"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-28707-9"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.51400\/2709-6998.1832"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3627673.3679854"},{"key":"e_1_3_2_2_23_1","volume-title":"Qianxiong Xu, Cheng Long, Shili Xiang, Gao Cong, Ziyue Li, and Rui Zhao.","author":"Liu Chenxi","year":"2025","unstructured":"Chenxi Liu, Kethmi Hirushini Hettige, Qianxiong Xu, Cheng Long, Shili Xiang, Gao Cong, Ziyue Li, and Rui Zhao. 2025. ST-LLM: Graph Enhanced Spatio-Temporal Large Language Models for Traffic Prediction. IEEE Transactions on Knowledge and Data Engineering (2025)."},{"key":"e_1_3_2_2_24_1","volume-title":"41th IEEE International Conference on Data Engineering.","author":"Liu Chenxi","year":"2025","unstructured":"Chenxi Liu, Hao Miao, Qianxiong Xu, Shaowen Zhou, Cheng Long, Yan Zhao, Ziyue Li, and Rui Zhao. 2025. Efficient Multivariate Time Series Forecasting via Calibrated Language Models with Privileged Knowledge Distillation. In 41th IEEE International Conference on Data Engineering."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i18.34067"},{"key":"e_1_3_2_2_26_1","volume-title":"Spatial-Temporal Large Language Model for Traffic Prediction. In 25th IEEE International Conference on Mobile Data Management. 31-40","author":"Liu Chenxi","year":"2024","unstructured":"Chenxi Liu, Sun Yang, Qianxiong Xu, Zhishuai Li, Cheng Long, Ziyue Li, and Rui Zhao. 2024. Spatial-Temporal Large Language Model for Traffic Prediction. In 25th IEEE International Conference on Mobile Data Management. 31-40."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.01.131"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412012"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645331"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i8.28728"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531975"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/1277741.1277845"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539281"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2019.08.201"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16556"},{"key":"e_1_3_2_2_36_1","volume-title":"A dirt-t approach to unsupervised domain adaptation. arXiv preprint arXiv:1802.08735","author":"Shu Rui","year":"2018","unstructured":"Rui Shu, Hung H Bui, Hirokazu Narui, and Stefano Ermon. 2018. A dirt-t approach to unsupervised domain adaptation. arXiv preprint arXiv:1802.08735 (2018)."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9005585"},{"key":"e_1_3_2_2_38_1","volume-title":"ACM Transactions on Information Systems","author":"Sun Ke","year":"2024","unstructured":"Ke Sun, Chenliang Li, and Tieyun Qian. 2024. City Matters! A Dual-Target Cross-City Sequential POI Recommendation Model. ACM Transactions on Information Systems (2024)."},{"key":"e_1_3_2_2_39_1","first-page":"3522","article-title":"GCRL: Efficient Delivery Area Assignment for Last-mile Logistics with Group-based Cooperative Reinforcement Learning. In 2023 IEEE 39th International Conference on Data Engineering (ICDE)","author":"Wang Hai","year":"2023","unstructured":"Hai Wang, Shuai Wang, Yu Yang, and Desheng Zhang. 2023. GCRL: Efficient Delivery Area Assignment for Last-mile Logistics with Group-based Cooperative Reinforcement Learning. In 2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE, 3522-3534.","journal-title":"IEEE"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i8.26215"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301774"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671548"},{"key":"e_1_3_2_2_43_1","volume-title":"Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121","author":"Wu Zonghan","year":"2019","unstructured":"Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019)."},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599766"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3690624.3709273"},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.08.021"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645595"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"crossref","unstructured":"Huaxiu Yao Yiding Liu Ying Wei Xianfeng Tang and Zhenhui Li. 2019. Learning from multiple cities: A meta-learning approach for spatial-temporal prediction. In The world wide web conference. 2181-2191.","DOI":"10.1145\/3308558.3313577"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2016.04.102"},{"key":"e_1_3_2_2_50_1","first-page":"1394","article-title":"TSec: An Efficient and Effective Framework for Time Series Classification. In 2024 IEEE 40th International Conference on Data Engineering (ICDE)","author":"Yao Yuanyuan","year":"2024","unstructured":"Yuanyuan Yao, Hailiang Jie, Lu Chen, Tianyi Li, Yunjun Gao, and Shiting Wen. 2024. TSec: An Efficient and Effective Framework for Time Series Classification. In 2024 IEEE 40th International Conference on Data Engineering (ICDE). IEEE, 1394-1406.","journal-title":"IEEE"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.14778\/3611540.3611561"},{"key":"e_1_3_2_2_52_1","volume-title":"Hierarchical graph representation learning with differentiable pooling. Advances in neural information processing systems 31","author":"Ying Zhitao","year":"2018","unstructured":"Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec. 2018. Hierarchical graph representation learning with differentiable pooling. Advances in neural information processing systems 31 (2018)."},{"key":"e_1_3_2_2_53_1","volume-title":"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875","author":"Yu Bing","year":"2017","unstructured":"Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017)."},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219922"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583304"},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599801"},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-00129-1_10"},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599377"},{"key":"e_1_3_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591642"},{"key":"e_1_3_2_2_60_1","first-page":"22243","article-title":"Meta-dmoe: Adapting to domain shift by meta-distillation from mixture-of-experts","volume":"35","author":"Zhong Tao","year":"2022","unstructured":"Tao Zhong, Zhixiang Chi, Li Gu, Yang Wang, Yuanhao Yu, and Jin Tang. 2022. Meta-dmoe: Adapting to domain shift by meta-distillation from mixture-of-experts. Advances in Neural Information Processing Systems 35 (2022), 22243-22257.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645391"},{"key":"e_1_3_2_2_62_1","first-page":"39","article-title":"A graphical and attentional framework for dual-target cross-domain recommendation","volume":"21","author":"Zhu Feng","year":"2020","unstructured":"Feng Zhu, Yan Wang, Chaochao Chen, Guanfeng Liu, and Xiaolin Zheng. 2020. A graphical and attentional framework for dual-target cross-domain recommendation. In IJCAI, Vol. 21. 39.","journal-title":"IJCAI"},{"key":"e_1_3_2_2_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498392"}],"event":{"name":"KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Toronto ON Canada","acronym":"KDD '25","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3711896.3737186","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,16]],"date-time":"2025-08-16T14:32:43Z","timestamp":1755354763000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3711896.3737186"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,3]]},"references-count":63,"alternative-id":["10.1145\/3711896.3737186","10.1145\/3711896"],"URL":"https:\/\/doi.org\/10.1145\/3711896.3737186","relation":{},"subject":[],"published":{"date-parts":[[2025,8,3]]},"assertion":[{"value":"2025-08-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}