{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:45:20Z","timestamp":1777614320970,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":27,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T00:00:00Z","timestamp":1628899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Research Grants Council (RGC) of Hong Kong","award":["21214720"],"award-info":[{"award-number":["21214720"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,8,14]]},"DOI":"10.1145\/3447548.3467170","type":"proceedings-article","created":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T18:21:39Z","timestamp":1628878899000},"page":"3735-3744","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Representation Learning for Predicting Customer Orders"],"prefix":"10.1145","author":[{"given":"Tongwen","family":"Wu","sequence":"first","affiliation":[{"name":"City University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Yang","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanzhi","family":"Li","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huiqiang","family":"Mao","sequence":"additional","affiliation":[{"name":"Tencent &amp; Alibaba Group, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liming","family":"Li","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hanzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoqing","family":"Wang","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hanzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuming","family":"Deng","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hanzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Discrete choice analysis: theory and application to travel demand","author":"Ben-Akiva Moshe","unstructured":"Moshe Ben-Akiva and Steven R Lerman . 1985. Discrete choice analysis: theory and application to travel demand . Vol. 9 . MIT press . Moshe Ben-Akiva and Steven R Lerman. 1985. Discrete choice analysis: theory and application to travel demand. Vol. 9. MIT press."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"crossref","unstructured":"Austin R. Benson Ravi Kumar and Andrew Tomkins. 2018a. A discrete choice model for subset selection. In WSDM. 37--45.  Austin R. Benson Ravi Kumar and Andrew Tomkins. 2018a. A discrete choice model for subset selection. In WSDM. 37--45.","DOI":"10.1145\/3159652.3159702"},{"key":"e_1_3_2_2_3_1","unstructured":"Austin R. Benson Ravi Kumar and Andrew Tomkins. 2018b. Sequences of sets. In KDD. 1148--1157.  Austin R. Benson Ravi Kumar and Andrew Tomkins. 2018b. Sequences of sets. In KDD. 1148--1157."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1287\/opre.2016.1505"},{"key":"e_1_3_2_2_5_1","series-title":"SIAM Journal on computing","volume-title":"An optimal algorithm for Monte Carlo estimation","author":"Dagum Paul","year":"2000","unstructured":"Paul Dagum , Richard Karp , Michael Luby , and Sheldon Ross . 2000. An optimal algorithm for Monte Carlo estimation . SIAM Journal on computing , Vol. 29 , 5 ( 2000 ), 1484--1496. Paul Dagum, Richard Karp, Michael Luby, and Sheldon Ross. 2000. An optimal algorithm for Monte Carlo estimation. SIAM Journal on computing, Vol. 29, 5 (2000), 1484--1496."},{"key":"e_1_3_2_2_6_1","volume-title":"A nonparametric approach to modeling choice with limited data. Management science","author":"Farias Vivek F.","year":"2013","unstructured":"Vivek F. Farias , Srikanth Jagabathula , and Devavrat Shah . 2013. A nonparametric approach to modeling choice with limited data. Management science , Vol. 59 , 2 ( 2013 ), 305--322. Vivek F. Farias, Srikanth Jagabathula, and Devavrat Shah. 2013. A nonparametric approach to modeling choice with limited data. Management science, Vol. 59, 2 (2013), 305--322."},{"key":"e_1_3_2_2_7_1","volume-title":"Data mining and knowledge discovery handbook","author":"Goethals Bart","unstructured":"Bart Goethals . 2005. Frequent set mining . In Data mining and knowledge discovery handbook . Springer , 377--397. Bart Goethals. 2005. Frequent set mining. In Data mining and knowledge discovery handbook. Springer, 377--397."},{"key":"e_1_3_2_2_8_1","unstructured":"R. L. Graham M. Gr\u00f6tschel and L. Lov\u00e1sz (Eds.). 1996. Handbook of Combinatorics (Vol. 2) .MIT Press Cambridge MA USA.  R. L. Graham M. Gr\u00f6tschel and L. Lov\u00e1sz (Eds.). 1996. Handbook of Combinatorics (Vol. 2) .MIT Press Cambridge MA USA."},{"key":"e_1_3_2_2_9_1","unstructured":"Haoji Hu and Xiangnan He. 2019. Sets2Sets: Learning from Sequential Sets with Neural Networks. In KDD. 1491--1499.  Haoji Hu and Xiangnan He. 2019. Sets2Sets: Learning from Sequential Sets with Neural Networks. In KDD. 1491--1499."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9574.1980.tb00681.x"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"crossref","unstructured":"Qi Liu Biao Xiang Enhong Chen Yong Ge Hui Xiong Tengfei Bao and Yi Zheng. 2012. Influential seed items recommendation. In RecSys. ACM 245--248.  Qi Liu Biao Xiang Enhong Chen Yong Ge Hui Xiong Tengfei Bao and Yi Zheng. 2012. Influential seed items recommendation. In RecSys. ACM 245--248.","DOI":"10.1145\/2365952.2366005"},{"key":"e_1_3_2_2_12_1","volume-title":"Proceedings of the Winter Simulation Conference","volume":"2","author":"Macro J. G.","unstructured":"J. G. Macro and R. E. Salmi . 2002. A simulation tool to determine warehouse efficiencies and storage allocations . In Proceedings of the Winter Simulation Conference , Vol. 2 . IEEE, 1274--1281. J. G. Macro and R. E. Salmi. 2002. A simulation tool to determine warehouse efficiencies and storage allocations. In Proceedings of the Winter Simulation Conference, Vol. 2. IEEE, 1274--1281."},{"key":"e_1_3_2_2_13_1","volume-title":"The \"shopping basket\": A model for multicategory purchase incidence decisions. Marketing science","author":"Manchanda Puneet","year":"1999","unstructured":"Puneet Manchanda , Asim Ansari , and Sunil Gupta . 1999. The \"shopping basket\": A model for multicategory purchase incidence decisions. Marketing science , Vol. 18 , 2 ( 1999 ), 95--114. Puneet Manchanda, Asim Ansari, and Sunil Gupta. 1999. The \"shopping basket\": A model for multicategory purchase incidence decisions. Marketing science, Vol. 18, 2 (1999), 95--114."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2013.6691742"},{"key":"e_1_3_2_2_15_1","volume-title":"Markov chains","author":"Norris James Robert","unstructured":"James Robert Norris . 1998. Markov chains . Cambridge university press . James Robert Norris. 1998. Markov chains. Cambridge university press."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"crossref","unstructured":"Steffen Rendle Christoph Freudenthaler and Lars Schmidt-Thieme. 2010. Factorizing personalized markov chains for next-basket recommendation. In WWW. ACM 811--820.  Steffen Rendle Christoph Freudenthaler and Lars Schmidt-Thieme. 2010. Factorizing personalized markov chains for next-basket recommendation. In WWW. ACM 811--820.","DOI":"10.1145\/1772690.1772773"},{"key":"e_1_3_2_2_17_1","unstructured":"John A Rice. 2006. Mathematical statistics and data analysis. Cengage Learning.  John A Rice. 2006. Mathematical statistics and data analysis. Cengage Learning."},{"key":"e_1_3_2_2_18_1","volume-title":"Efficient discovery of association rules and frequent itemsets through sampling with tight performance guarantees. ACM Transactions on Knowledge Discovery from Data","author":"Riondato Matteo","year":"2014","unstructured":"Matteo Riondato and Eli Upfal . 2014. Efficient discovery of association rules and frequent itemsets through sampling with tight performance guarantees. ACM Transactions on Knowledge Discovery from Data , Vol. 8 , 4 ( 2014 ), 20. Matteo Riondato and Eli Upfal. 2014. Efficient discovery of association rules and frequent itemsets through sampling with tight performance guarantees. ACM Transactions on Knowledge Discovery from Data, Vol. 8, 4 (2014), 20."},{"key":"e_1_3_2_2_19_1","volume-title":"Blei","author":"Rudolph Maja","year":"2016","unstructured":"Maja Rudolph , Francisco J. R. Ruiz , Stephan Mandt , and David M . Blei . 2016 . Exponential family embeddings. In NIPS. 478--486. Maja Rudolph, Francisco J. R. Ruiz, Stephan Mandt, and David M. Blei. 2016. Exponential family embeddings. In NIPS. 478--486."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-4359(00)00030-0"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"crossref","unstructured":"Leilei Sun Yansong Bai Bowen Du Chuanren Liu Hui Xiong and Weifeng Lv. 2020. Dual sequential network for temporal sets prediction. In SIGIR. 1439--1448.  Leilei Sun Yansong Bai Bowen Du Chuanren Liu Hui Xiong and Weifeng Lv. 2020. Dual sequential network for temporal sets prediction. In SIGIR. 1439--1448.","DOI":"10.1145\/3397271.3401124"},{"key":"e_1_3_2_2_22_1","volume-title":"Discrete choice methods with simulation","author":"Train Kenneth E","unstructured":"Kenneth E Train . 2009. Discrete choice methods with simulation . Cambridge university press . Kenneth E Train. 2009. Discrete choice methods with simulation .Cambridge university press."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"crossref","unstructured":"Pengfei Wang Jiafeng Guo Yanyan Lan Jun Xu Shengxian Wan and Xueqi Cheng. 2015. Learning hierarchical representation model for nextbasket recommendation. In SIGIR. ACM 403--412.  Pengfei Wang Jiafeng Guo Yanyan Lan Jun Xu Shengxian Wan and Xueqi Cheng. 2015. Learning hierarchical representation model for nextbasket recommendation. In SIGIR. ACM 403--412.","DOI":"10.1145\/2766462.2767694"},{"key":"e_1_3_2_2_24_1","volume-title":"49th International Conference on Computers & Industrial Engineering .","author":"Wu Tongwen","year":"2019","unstructured":"Tongwen Wu , Huiqiang Mao , Yanzhi Li , and Di Chen . 2019 . Assortment selection for a frontend warehouse: a robust data-driven approach . In 49th International Conference on Computers & Industrial Engineering . Tongwen Wu, Huiqiang Mao, Yanzhi Li, and Di Chen. 2019. Assortment selection for a frontend warehouse: a robust data-driven approach. In 49th International Conference on Computers & Industrial Engineering ."},{"key":"e_1_3_2_2_25_1","unstructured":"Feng Yu Qiang Liu Shu Wu Liang Wang and Tieniu Tan. 2016. A dynamic recurrent model for next basket recommendation. In SIGIR. ACM 729--732.  Feng Yu Qiang Liu Shu Wu Liang Wang and Tieniu Tan. 2016. A dynamic recurrent model for next basket recommendation. In SIGIR. ACM 729--732."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"crossref","unstructured":"Le Yu Leilei Sun Bowen Du Chuanren Liu Hui Xiong and Weifeng Lv. 2020. Predicting temporal sets with deep neural networks. In SIGKDD. 1083--1091.  Le Yu Leilei Sun Bowen Du Chuanren Liu Hui Xiong and Weifeng Lv. 2020. Predicting temporal sets with deep neural networks. In SIGKDD. 1083--1091.","DOI":"10.1145\/3394486.3403152"},{"key":"e_1_3_2_2_27_1","unstructured":"Manzil Zaheer Satwik Kottur Siamak Ravanbakhsh Barnabas Poczos Ruslan Salakhutdinov and Alexander Smola. 2017. Deep sets. In NIPS. 3391--3401.  Manzil Zaheer Satwik Kottur Siamak Ravanbakhsh Barnabas Poczos Ruslan Salakhutdinov and Alexander Smola. 2017. Deep sets. In NIPS. 3391--3401."}],"event":{"name":"KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event Singapore","acronym":"KDD '21","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 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467170","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467170","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:27Z","timestamp":1750191507000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467170"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,14]]},"references-count":27,"alternative-id":["10.1145\/3447548.3467170","10.1145\/3447548"],"URL":"https:\/\/doi.org\/10.1145\/3447548.3467170","relation":{},"subject":[],"published":{"date-parts":[[2021,8,14]]},"assertion":[{"value":"2021-08-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}