{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T17:52:20Z","timestamp":1770227540249,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":35,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T00:00:00Z","timestamp":1635206400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Alibaba-NTU Singapore Joint Research Institute"},{"name":"Alibaba Group"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,10,26]]},"DOI":"10.1145\/3459637.3481960","type":"proceedings-article","created":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T15:53:43Z","timestamp":1636991623000},"page":"3935-3944","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Unsupervised Categorical Representation Learning for Package Arrival Time Prediction"],"prefix":"10.1145","author":[{"given":"Yang","family":"Li","sequence":"first","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}]},{"given":"Xingyu","family":"Wu","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}]},{"given":"Jinglong","family":"Wang","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}]},{"given":"Yong","family":"Liu","sequence":"additional","affiliation":[{"name":"Alibaba-NTU Singapore Joint Research Institute, Singapore, Singapore"}]},{"given":"Xiaoqing","family":"Wang","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}]},{"given":"Yuming","family":"Deng","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}]},{"given":"Chunyan","family":"Miao","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}]}],"member":"320","published-online":{"date-parts":[[2021,10,30]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1376616.1376746"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2807452"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2988450.2988454"},{"key":"e_1_3_2_2_4_1","unstructured":"Jeff Donahue Philipp Kr\u00e4henb\u00fchl and Trevor Darrell. 2017. Adversarial Feature Learning. arXiv:1605.09782 [cs.LG]  Jeff Donahue Philipp Kr\u00e4henb\u00fchl and Trevor Darrell. 2017. Adversarial Feature Learning. arXiv:1605.09782 [cs.LG]"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098036"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1983.10478008"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.5555\/3086952"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_2_9_1","unstructured":"Huifeng Guo Ruiming Tang Yunming Ye Zhenguo Li and Xiuqiang He. 2017. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. arXiv e-prints (March 2017) arXiv:1703.04247.  Huifeng Guo Ruiming Tang Yunming Ye Zhenguo Li and Xiuqiang He. 2017. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. arXiv e-prints (March 2017) arXiv:1703.04247."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.5555\/3172077.3172157"},{"key":"e_1_3_2_2_11_1","volume-title":"Principal Component Analysis","author":"Jolliffe I. T."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959134"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2002.1017616"},{"key":"e_1_3_2_2_14_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2017"},{"key":"e_1_3_2_2_15_1","unstructured":"Diederik P Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes. arXiv:1312.6114 [stat.ML]  Diederik P Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes. arXiv:1312.6114 [stat.ML]"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313418"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220033"},{"key":"e_1_3_2_2_18_1","volume-title":"Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. arXiv e-prints (July","author":"Li Yaguang","year":"2017"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357951"},{"key":"e_1_3_2_2_20_1","unstructured":"Tomas Mikolov Kai Chen Greg S. Corrado and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space.  Tomas Mikolov Kai Chen Greg S. Corrado and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3455008"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"crossref","unstructured":"J. Pearl. 2009. Causality. Cambridge University Press.  J. Pearl. 2009. Causality. Cambridge University Press.","DOI":"10.1017\/CBO9780511803161"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2010.127"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357925"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219869"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3124749.3124754"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219900"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.5555\/3045390.3045396"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.5555\/3060832.3060941"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358014"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-30671-1_4"},{"key":"e_1_3_2_2_34_1","volume-title":"Unsupervised Hetero-geneous Coupling Learning for Categorical Representation","author":"Zhu Chengzhang","year":"2020"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"crossref","unstructured":"Lin Zhu Wei Yu Kairong Zhou and etal 2020. Order Fulfillment Cycle Time Estimation for On-Demand Food Delivery. Association for Computing Machinery 2571--2580.  Lin Zhu Wei Yu Kairong Zhou and et al. 2020. Order Fulfillment Cycle Time Estimation for On-Demand Food Delivery. Association for Computing Machinery 2571--2580.","DOI":"10.1145\/3394486.3403307"}],"event":{"name":"CIKM '21: The 30th ACM International Conference on Information and Knowledge Management","location":"Virtual Event Queensland Australia","acronym":"CIKM '21","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 30th ACM International Conference on Information &amp; Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3459637.3481960","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3459637.3481960","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:48:59Z","timestamp":1750193339000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3459637.3481960"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,26]]},"references-count":35,"alternative-id":["10.1145\/3459637.3481960","10.1145\/3459637"],"URL":"https:\/\/doi.org\/10.1145\/3459637.3481960","relation":{},"subject":[],"published":{"date-parts":[[2021,10,26]]},"assertion":[{"value":"2021-10-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}