{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:57:32Z","timestamp":1777615052209,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":32,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,9,14]],"date-time":"2023-09-14T00:00:00Z","timestamp":1694649600000},"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":[[2023,9,14]]},"DOI":"10.1145\/3604915.3608772","type":"proceedings-article","created":{"date-parts":[[2023,9,14]],"date-time":"2023-09-14T22:40:23Z","timestamp":1694731223000},"page":"151-160","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Task Aware Feature Extraction Framework for Sequential Dependence Multi-Task Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-9114-7668","authenticated-orcid":false,"given":"Xuewen","family":"Tao","sequence":"first","affiliation":[{"name":"Mybank, Ant Group, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2901-9608","authenticated-orcid":false,"given":"Mingming","family":"Ha","sequence":"additional","affiliation":[{"name":"Mybank, Ant Group, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2465-4941","authenticated-orcid":false,"given":"Qiongxu","family":"Ma","sequence":"additional","affiliation":[{"name":"Mybank, Ant Group, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8749-5912","authenticated-orcid":false,"given":"Hongwei","family":"Cheng","sequence":"additional","affiliation":[{"name":"Mybank, Ant Group, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7398-4706","authenticated-orcid":false,"given":"Wenfang","family":"Lin","sequence":"additional","affiliation":[{"name":"Mybank, Ant Group, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6817-626X","authenticated-orcid":false,"given":"Xiaobo","family":"Guo","sequence":"additional","affiliation":[{"name":"Institute of Information Science, Beijing Jiaotong Univeristy; Mybank, Ant Group, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3764-737X","authenticated-orcid":false,"given":"Linxun","family":"Cheng","sequence":"additional","affiliation":[{"name":"Mybank, Ant Group, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8051-1278","authenticated-orcid":false,"given":"Bing","family":"Han","sequence":"additional","affiliation":[{"name":"Mybank, Ant Group, China"}]}],"member":"320","published-online":{"date-parts":[[2023,9,14]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Machine learning 28, 1","author":"Baxter Jonathan","year":"1997","unstructured":"Jonathan Baxter. 1997. A Bayesian\/information theoretic model of learning to learn via multiple task sampling. Machine learning 28, 1 (1997), 7\u201339."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Ling Chen Donghui Chen Fan Yang and Jianling Sun. 2021. Neural episodic control. In A deep multi-task representation learning method for time series classification and retrieval. Information Sciences 17\u201332.","DOI":"10.1016\/j.ins.2020.12.062"},{"key":"e_1_3_2_1_3_1","volume-title":"Multi-task learning with deep neural networks: A survey. arXiv preprint arXiv:2009.09796","author":"Crawshaw Michael","year":"2020","unstructured":"Michael Crawshaw. 2020. Multi-task learning with deep neural networks: A survey. arXiv preprint arXiv:2009.09796 (2020)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3463116"},{"key":"e_1_3_2_1_5_1","unstructured":"Tiankai Gu Kun Kuang Hong Zhu Jingjie Li Zhenhua Dong Wenjie Hu Zhenguo Li Xiuqiang He and Yue Liu. 2021. Estimating true post-click conversion via group-stratified counterfactual inference."},{"key":"e_1_3_2_1_6_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 preprint arXiv:1703.04247 (2017)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462917"},{"key":"e_1_3_2_1_8_1","unstructured":"H. Hazimeh Z. Zhao A. Chowdhery M. Sathiamoorthy and E.\u00a0H. Chi. 2021. DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning. (2021)."},{"key":"e_1_3_2_1_9_1","volume-title":"Adaptive mixtures of local experts. Neural computation 3, 1","author":"Jacobs A","year":"1991","unstructured":"Robert\u00a0A Jacobs, Michael\u00a0I Jordan, Steven\u00a0J Nowlan, and Geoffrey\u00a0E Hinton. 1991. Adaptive mixtures of local experts. Neural computation 3, 1 (1991), 79\u201387."},{"key":"e_1_3_2_1_10_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma P","year":"2014","unstructured":"Diederik\u00a0P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_1_11_1","volume-title":"International Conference on Machine Learning. PMLR, 3744\u20133753","author":"Lee Juho","year":"2019","unstructured":"Juho Lee, Yoonho Lee, Jungtaek Kim, Adam Kosiorek, Seungjin Choi, and Yee\u00a0Whye Teh. 2019. Set transformer: A framework for attention-based permutation-invariant neural networks. In International Conference on Machine Learning. PMLR, 3744\u20133753."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.2307\/2346413"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220007"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210104"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.433"},{"key":"e_1_3_2_1_16_1","volume-title":"An Analysis Of Entire Space Multi-Task Models For Post-Click Conversion Prediction. In Fifteenth ACM Conference on Recommender Systems. 613\u2013619","author":"O\u2019Brien Conor","year":"2021","unstructured":"Conor O\u2019Brien, Kin\u00a0Sum Liu, James Neufeld, Rafael Barreto, and Jonathan\u00a0J Hunt. 2021. An Analysis Of Entire Space Multi-Task Models For Post-Click Conversion Prediction. In Fifteenth ACM Conference on Recommender Systems. 613\u2013619."},{"key":"e_1_3_2_1_17_1","volume-title":"International Conference on Machine Learning. PMLR, 2827\u20132836","author":"Pritzel Alexander","year":"2017","unstructured":"Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adria\u00a0Puigdomenech Badia, Oriol Vinyals, Demis Hassabis, Daan Wierstra, and Charles Blundell. 2017. Neural episodic control. In International Conference on Machine Learning. PMLR, 2827\u20132836."},{"key":"e_1_3_2_1_18_1","volume-title":"Multitask Learning. Machine Learning","author":"Caruana Rich","year":"1997","unstructured":"Rich and Caruana. 1997. Multitask Learning. Machine Learning (1997)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014822"},{"key":"e_1_3_2_1_20_1","first-page":"21031","article-title":"Variational multi-task learning with gumbel-softmax priors","volume":"34","author":"Shen Jiayi","year":"2021","unstructured":"Jiayi Shen, Xiantong Zhen, Marcel Worring, and Ling Shao. 2021. Variational multi-task learning with gumbel-softmax priors. Advances in Neural Information Processing Systems 34 (2021), 21031\u201321042.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_21_1","volume-title":"International Conference on Machine Learning. PMLR, 5986\u20135995","author":"Stickland Asa\u00a0Cooper","year":"2019","unstructured":"Asa\u00a0Cooper Stickland and Iain Murray. 2019. Bert and pals: Projected attention layers for efficient adaptation in multi-task learning. In International Conference on Machine Learning. PMLR, 5986\u20135995."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3412236"},{"key":"e_1_3_2_1_23_1","volume-title":"A Brief Review of Deep Multi-task Learning and Auxiliary Task Learning. arXiv preprint arXiv:2007.01126","author":"Vafaeikia Partoo","year":"2020","unstructured":"Partoo Vafaeikia, Khashayar Namdar, and Farzad Khalvati. 2020. A Brief Review of Deep Multi-task Learning and Auxiliary Task Learning. arXiv preprint arXiv:2007.01126 (2020)."},{"key":"e_1_3_2_1_24_1","volume-title":"Revisiting multi-task learning in the deep learning era. arXiv preprint arXiv:2004.13379 2","author":"Vandenhende Simon","year":"2020","unstructured":"Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans, Dengxin Dai, and Luc Van\u00a0Gool. 2020. Revisiting multi-task learning in the deep learning era. arXiv preprint arXiv:2004.13379 2 (2020)."},{"key":"e_1_3_2_1_25_1","volume-title":"Enhancing CTR Prediction with Context-Aware Feature Representation Learning. arXiv preprint arXiv:2204.08758","author":"Wang Fangye","year":"2022","unstructured":"Fangye Wang, Yingxu Wang, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, and Ning Gu. 2022. Enhancing CTR Prediction with Context-Aware Feature Representation Learning. arXiv preprint arXiv:2204.08758 (2022)."},{"key":"e_1_3_2_1_26_1","volume-title":"ESCM2: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation. arXiv preprint arXiv:2204.05125","author":"Wang Hao","year":"2022","unstructured":"Hao Wang, Tai-Wei Chang, Tianqiao Liu, Jianmin Huang, Zhichao Chen, Chao Yu, Ruopeng Li, and Wei Chu. 2022. ESCM2: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation. arXiv preprint arXiv:2204.05125 (2022)."},{"key":"e_1_3_2_1_27_1","volume-title":"K-adapter: Infusing knowledge into pre-trained models with adapters. arXiv preprint arXiv:2002.01808","author":"Wang Ruize","year":"2020","unstructured":"Ruize Wang, Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Guihong Cao, Daxin Jiang, Ming Zhou, 2020. K-adapter: Infusing knowledge into pre-trained models with adapters. arXiv preprint arXiv:2002.01808 (2020)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3463053"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401443"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467071"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380037"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330861"}],"event":{"name":"RecSys '23: Seventeenth ACM Conference on Recommender Systems","location":"Singapore Singapore","acronym":"RecSys '23","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGAI ACM Special Interest Group on Artificial Intelligence","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGIR ACM Special Interest Group on Information Retrieval","SIGCHI ACM Special Interest Group on Computer-Human Interaction","SIGecom Special Interest Group on Economics and Computation"]},"container-title":["Proceedings of the 17th ACM Conference on Recommender Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3604915.3608772","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3604915.3608772","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:46:06Z","timestamp":1750178766000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3604915.3608772"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,14]]},"references-count":32,"alternative-id":["10.1145\/3604915.3608772","10.1145\/3604915"],"URL":"https:\/\/doi.org\/10.1145\/3604915.3608772","relation":{},"subject":[],"published":{"date-parts":[[2023,9,14]]},"assertion":[{"value":"2023-09-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}