{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T10:17:25Z","timestamp":1775470645921,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":41,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T00:00:00Z","timestamp":1657065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,7,6]]},"DOI":"10.1145\/3477495.3532050","type":"proceedings-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T15:12:08Z","timestamp":1657206728000},"page":"814-824","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":29,"title":["RankFlow"],"prefix":"10.1145","author":[{"given":"Jiarui","family":"Qin","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"given":"Jiachen","family":"Zhu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"given":"Bo","family":"Chen","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Shenzhen, China"}]},{"given":"Zhirong","family":"Liu","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Shenzhen, China"}]},{"given":"Weiwen","family":"Liu","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Shenzhen, China"}]},{"given":"Ruiming","family":"Tang","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Shenzhen, China"}]},{"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[{"name":"ruizhang.info, Shenzhen, China"}]},{"given":"Yong","family":"Yu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"given":"Weinan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]}],"member":"320","published-online":{"date-parts":[[2022,7,7]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"2020. MindSpore. https:\/\/www.mindspore.cn\/  2020. MindSpore. https:\/\/www.mindspore.cn\/"},{"key":"e_1_3_2_2_2_1","first-page":"23","article-title":"From ranknet to lambdarank to lambdamart: An overview","volume":"11","author":"Burges Christopher JC","year":"2010","unstructured":"Christopher JC Burges . 2010 . From ranknet to lambdarank to lambdamart: An overview . Learning , Vol. 11 , 23 -- 581 (2010), 81. Christopher JC Burges. 2010. From ranknet to lambdarank to lambdamart: An overview. Learning, Vol. 11, 23--581 (2010), 81.","journal-title":"Learning"},{"key":"e_1_3_2_2_3_1","volume-title":"Bias and debias in recommender system: A survey and future directions. arXiv preprint arXiv:2010.03240","author":"Chen Jiawei","year":"2020","unstructured":"Jiawei Chen , Hande Dong , Xiang Wang , Fuli Feng , Meng Wang , and Xiangnan He. 2020. Bias and debias in recommender system: A survey and future directions. arXiv preprint arXiv:2010.03240 ( 2020 ). Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2020. Bias and debias in recommender system: A survey and future directions. arXiv preprint arXiv:2010.03240 (2020)."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"crossref","unstructured":"Ruey-Cheng Chen Luke Gallagher Roi Blanco and J Shane Culpepper. 2017. Efficient cost-aware cascade ranking in multi-stage retrieval. In SIGIR. 445--454.  Ruey-Cheng Chen Luke Gallagher Roi Blanco and J Shane Culpepper. 2017. Efficient cost-aware cascade ranking in multi-stage retrieval. In SIGIR. 445--454.","DOI":"10.1145\/3077136.3080819"},{"key":"e_1_3_2_2_5_1","volume-title":"et almbox","author":"Cheng Heng-Tze","year":"2016","unstructured":"Heng-Tze Cheng , Levent Koc , Jeremiah Harmsen , Tal Shaked , Tushar Chandra , Hrishi Aradhye , Glen Anderson , Greg Corrado , Wei Chai , Mustafa Ispir , et almbox . 2016 . Wide & deep learning for recommender systems. In DLRS. 7--10. Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et almbox. 2016. Wide & deep learning for recommender systems. In DLRS. 7--10."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"crossref","unstructured":"Ying Cui Ruofei Zhang Wei Li and Jianchang Mao. 2011. Bid landscape forecasting in online ad exchange marketplace. In KDD.  Ying Cui Ruofei Zhang Wei Li and Jianchang Mao. 2011. Bid landscape forecasting in online ad exchange marketplace. In KDD.","DOI":"10.1145\/2020408.2020454"},{"key":"e_1_3_2_2_8_1","volume-title":"Internet advertising and the generalized second-price auction: Selling billions of dollars worth of keywords. American economic review","author":"Edelman Benjamin","year":"2007","unstructured":"Benjamin Edelman , Michael Ostrovsky , and Michael Schwarz . 2007. Internet advertising and the generalized second-price auction: Selling billions of dollars worth of keywords. American economic review , Vol. 97 , 1 ( 2007 ), 242--259. Benjamin Edelman, Michael Ostrovsky, and Michael Schwarz. 2007. Internet advertising and the generalized second-price auction: Selling billions of dollars worth of keywords. American economic review, Vol. 97, 1 (2007), 242--259."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"crossref","unstructured":"Miao Fan Jiacheng Guo Shuai Zhu Shuo Miao Mingming Sun and Ping Li. 2019. MOBIUS: towards the next generation of query-ad matching in baidu's sponsored search. In SIGKDD. 2509--2517.  Miao Fan Jiacheng Guo Shuai Zhu Shuo Miao Mingming Sun and Ping Li. 2019. MOBIUS: towards the next generation of query-ad matching in baidu's sponsored search. In SIGKDD. 2509--2517.","DOI":"10.1145\/3292500.3330651"},{"key":"e_1_3_2_2_10_1","unstructured":"Hongliang Fei Jingyuan Zhang Xingxuan Zhou Junhao Zhao Xinyang Qi and Ping Li. 2021. GemNN: Gating-enhanced Multi-task Neural Networks with Feature Interaction Learning for CTR Prediction. In SIGIR. 2166--2171.  Hongliang Fei Jingyuan Zhang Xingxuan Zhou Junhao Zhao Xinyang Qi and Ping Li. 2021. GemNN: Gating-enhanced Multi-task Neural Networks with Feature Interaction Learning for CTR Prediction. In SIGIR. 2166--2171."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"crossref","unstructured":"Luke Gallagher Ruey-Cheng Chen Roi Blanco and J Shane Culpepper. 2019. Joint optimization of cascade ranking models. In WSDM. 15--23.  Luke Gallagher Ruey-Cheng Chen Roi Blanco and J Shane Culpepper. 2019. Joint optimization of cascade ranking models. In WSDM. 15--23.","DOI":"10.1145\/3289600.3290986"},{"key":"e_1_3_2_2_12_1","unstructured":"Huifeng Guo Ruiming Tang Yunming Ye Zhenguo Li and Xiuqiang He. 2017. Deepfm: a factorization-machine based neural network for ctr prediction. In IJCAI.  Huifeng Guo Ruiming Tang Yunming Ye Zhenguo Li and Xiuqiang He. 2017. Deepfm: a factorization-machine based neural network for ctr prediction. In IJCAI."},{"key":"e_1_3_2_2_13_1","unstructured":"Bal\u00e1zs Hidasi Alexandros Karatzoglou Linas Baltrunas and Domonkos Tikk. 2016. Session-based recommendations with recurrent neural networks. In ICLR.  Bal\u00e1zs Hidasi Alexandros Karatzoglou Linas Baltrunas and Domonkos Tikk. 2016. Session-based recommendations with recurrent neural networks. In ICLR."},{"key":"e_1_3_2_2_14_1","volume-title":"Advances in Neural Information Processing Systems","volume":"34","author":"Hron Jiri","year":"2021","unstructured":"Jiri Hron , Karl Krauth , Michael Jordan , and Niki Kilbertus . 2021 . On component interactions in two-stage recommender systems . Advances in Neural Information Processing Systems , Vol. 34 (2021). Jiri Hron, Karl Krauth, Michael Jordan, and Niki Kilbertus. 2021. On component interactions in two-stage recommender systems. Advances in Neural Information Processing Systems, Vol. 34 (2021)."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403305"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2505515.2505665"},{"key":"e_1_3_2_2_17_1","unstructured":"Tie-Yan Liu. 2011. Learning to rank for information retrieval. (2011).  Tie-Yan Liu. 2011. Learning to rank for information retrieval. (2011)."},{"key":"e_1_3_2_2_18_1","volume-title":"et almbox","author":"Liu Xiangyu","year":"2021","unstructured":"Xiangyu Liu , Chuan Yu , Zhilin Zhang , Zhenzhe Zheng , Yu Rong , Hongtao Lv , Da Huo , Yiqing Wang , Dagui Chen , Jian Xu , et almbox . 2021 . Neural Auction : End-to-End Learning of Auction Mechanisms for E-Commerce Advertising . arXiv preprint arXiv:2106.03593 (2021). Xiangyu Liu, Chuan Yu, Zhilin Zhang, Zhenzhe Zheng, Yu Rong, Hongtao Lv, Da Huo, Yiqing Wang, Dagui Chen, Jian Xu, et almbox. 2021. Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising. arXiv preprint arXiv:2106.03593 (2021)."},{"key":"e_1_3_2_2_19_1","volume-title":"Towards a Better Tradeoff between Effectiveness and Efficiency in Pre-Ranking: A Learnable Feature Selection based Approach. arXiv preprint arXiv:2105.07706","author":"Ma Xu","year":"2021","unstructured":"Xu Ma , Pengjie Wang , Hui Zhao , Shaoguo Liu , Chuhan Zhao , Wei Lin , Kuang-Chih Lee , Jian Xu , and Bo Zheng . 2021. Towards a Better Tradeoff between Effectiveness and Efficiency in Pre-Ranking: A Learnable Feature Selection based Approach. arXiv preprint arXiv:2105.07706 ( 2021 ). Xu Ma, Pengjie Wang, Hui Zhao, Shaoguo Liu, Chuhan Zhao, Wei Lin, Kuang-Chih Lee, Jian Xu, and Bo Zheng. 2021. Towards a Better Tradeoff between Effectiveness and Efficiency in Pre-Ranking: A Learnable Feature Selection based Approach. arXiv preprint arXiv:2105.07706 (2021)."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401104"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3298689.3347000"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"crossref","unstructured":"Qi Pi Weijie Bian Guorui Zhou Xiaoqiang Zhu and Kun Gai. 2019. Practice on long sequential user behavior modeling for click-through rate prediction. In KDD. 2671--2679.  Qi Pi Weijie Bian Guorui Zhou Xiaoqiang Zhu and Kun Gai. 2019. Practice on long sequential user behavior modeling for click-through rate prediction. In KDD. 2671--2679.","DOI":"10.1145\/3292500.3330666"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"crossref","unstructured":"Pi Qi Xiaoqiang Zhu Guorui Zhou Yujing Zhang Zhe Wang Lejian Ren Ying Fan and Kun Gai. 2020. Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction. In CIKM.  Pi Qi Xiaoqiang Zhu Guorui Zhou Yujing Zhang Zhe Wang Lejian Ren Ying Fan and Kun Gai. 2020. Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction. In CIKM.","DOI":"10.1145\/3340531.3412744"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467216"},{"key":"e_1_3_2_2_25_1","unstructured":"Jiarui Qin W. Zhang Xin Wu Jiarui Jin Yuchen Fang and Y. Yu. 2020. User Behavior Retrieval for Click-Through Rate Prediction. In SIGIR.  Jiarui Qin W. Zhang Xin Wu Jiarui Jin Yuchen Fang and Y. Yu. 2020. User Behavior Retrieval for Click-Through Rate Prediction. In SIGIR."},{"key":"e_1_3_2_2_26_1","unstructured":"Yanru Qu Han Cai Kan Ren Weinan Zhang Yong Yu Ying Wen and Jun Wang. 2016. Product-based neural networks for user response prediction. In ICDM.  Yanru Qu Han Cai Kan Ren Weinan Zhang Yong Yu Ying Wen and Jun Wang. 2016. Product-based neural networks for user response prediction. In ICDM."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"crossref","unstructured":"Steffen Rendle. 2010. Factorization machines. In ICDM.  Steffen Rendle. 2010. Factorization machines. In ICDM.","DOI":"10.1109\/ICDM.2010.127"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220021"},{"key":"e_1_3_2_2_29_1","unstructured":"Jun Wang and Shuai Yuan. 2013. Real-time bidding: A new frontier of computational advertising research. In CIKM Tutorial.  Jun Wang and Shuai Yuan. 2013. Real-time bidding: A new frontier of computational advertising research. In CIKM Tutorial."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/2009916.2009934"},{"key":"e_1_3_2_2_31_1","volume-title":"Cold: Towards the next generation of pre-ranking system. arXiv preprint arXiv:2007.16122","author":"Wang Zhe","year":"2020","unstructured":"Zhe Wang , Liqin Zhao , Biye Jiang , Guorui Zhou , Xiaoqiang Zhu , and Kun Gai . 2020 . Cold: Towards the next generation of pre-ranking system. arXiv preprint arXiv:2007.16122 (2020). Zhe Wang, Liqin Zhao, Biye Jiang, Guorui Zhou, Xiaoqiang Zhu, and Kun Gai. 2020. Cold: Towards the next generation of pre-ranking system. arXiv preprint arXiv:2007.16122 (2020)."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403309"},{"key":"e_1_3_2_2_33_1","volume-title":"International conference on machine learning. PMLR, 133--141","author":"Xu Zhixiang","year":"2013","unstructured":"Zhixiang Xu , Matt Kusner , Kilian Weinberger , and Minmin Chen . 2013 . Cost-sensitive tree of classifiers . In International conference on machine learning. PMLR, 133--141 . Zhixiang Xu, Matt Kusner, Kilian Weinberger, and Minmin Chen. 2013. Cost-sensitive tree of classifiers. In International conference on machine learning. PMLR, 133--141."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2670319"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358058"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"crossref","unstructured":"Weinan Zhang Jiarui Qin Wei Guo Ruiming Tang and Xiuqiang He. 2021. Deep Learning for Click-Through Rate Estimation. In IJCAI.  Weinan Zhang Jiarui Qin Wei Guo Ruiming Tang and Xiuqiang He. 2021. Deep Learning for Click-Through Rate Estimation. In IJCAI.","DOI":"10.24963\/ijcai.2021\/636"},{"key":"e_1_3_2_2_37_1","volume-title":"Distillation based Multi-task Learning: A Candidate Generation Model for Improving Reading Duration. arXiv preprint arXiv:2102.07142","author":"Zhao Zhong","year":"2021","unstructured":"Zhong Zhao , Yanmei Fu , Hanming Liang , Li Ma , Guangyao Zhao , and Hongwei Jiang . 2021. Distillation based Multi-task Learning: A Candidate Generation Model for Improving Reading Duration. arXiv preprint arXiv:2102.07142 ( 2021 ). Zhong Zhao, Yanmei Fu, Hanming Liang, Li Ma, Guangyao Zhao, and Hongwei Jiang. 2021. Distillation based Multi-task Learning: A Candidate Generation Model for Improving Reading Duration. arXiv preprint arXiv:2102.07142 (2021)."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015941"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"crossref","unstructured":"Guorui Zhou Xiaoqiang Zhu Chenru Song Ying Fan Han Zhu Xiao Ma Yanghui Yan Junqi Jin Han Li and Kun Gai. 2018. Deep interest network for click-through rate prediction. In KDD.  Guorui Zhou Xiaoqiang Zhu Chenru Song Ying Fan Han Zhu Xiao Ma Yanghui Yan Junqi Jin Han Li and Kun Gai. 2018. Deep interest network for click-through rate prediction. In KDD.","DOI":"10.1145\/3219819.3219823"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"crossref","unstructured":"Han Zhu Xiang Li Pengye Zhang Guozheng Li Jie He Han Li and Kun Gai. 2018. Learning Tree-based Deep Model for Recommender Systems. In KDD.  Han Zhu Xiang Li Pengye Zhang Guozheng Li Jie He Han Li and Kun Gai. 2018. Learning Tree-based Deep Model for Recommender Systems. In KDD.","DOI":"10.1145\/3219819.3219826"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412704"}],"event":{"name":"SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Madrid Spain","acronym":"SIGIR '22","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3477495.3532050","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3477495.3532050","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:10:35Z","timestamp":1750183835000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3477495.3532050"}},"subtitle":["Joint Optimization of Multi-Stage Cascade Ranking Systems as Flows"],"short-title":[],"issued":{"date-parts":[[2022,7,6]]},"references-count":41,"alternative-id":["10.1145\/3477495.3532050","10.1145\/3477495"],"URL":"https:\/\/doi.org\/10.1145\/3477495.3532050","relation":{},"subject":[],"published":{"date-parts":[[2022,7,6]]},"assertion":[{"value":"2022-07-07","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}