{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:08:02Z","timestamp":1765544882821,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":35,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:00:00Z","timestamp":1665964800000},"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":[[2022,10,17]]},"DOI":"10.1145\/3511808.3557106","type":"proceedings-article","created":{"date-parts":[[2022,10,16]],"date-time":"2022-10-16T01:29:57Z","timestamp":1665883797000},"page":"3684-3693","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["KEEP: An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging"],"prefix":"10.1145","author":[{"given":"Yujing","family":"Zhang","sequence":"first","affiliation":[{"name":"Alibaba Group, Beijing, China"}]},{"given":"Zhangming","family":"Chan","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}]},{"given":"Shuhao","family":"Xu","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"given":"Weijie","family":"Bian","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}]},{"given":"Shuguang","family":"Han","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}]},{"given":"Hongbo","family":"Deng","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}]},{"given":"Bo","family":"Zheng","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"CAN: Feature Co-Action for Click-Through Rate Prediction. arXiv e-prints","author":"Bian Weijie","year":"2020","unstructured":"Weijie Bian , Kailun Wu , Lejian Ren , 2020 . CAN: Feature Co-Action for Click-Through Rate Prediction. arXiv e-prints (2020). Weijie Bian, Kailun Wu, Lejian Ren, et al. 2020. CAN: Feature Co-Action for Click-Through Rate Prediction. arXiv e-prints (2020)."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btab133"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3119619"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3326937.3341261"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_3_2_2_6_1","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2019 . BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding . In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics . Minneapolis, MN, USA, 4171--4186. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics. Minneapolis, MN, USA, 4171--4186."},{"key":"e_1_3_2_2_7_1","unstructured":"Dumitru Erhan Pierre-Antoine Manzagol Yoshua Bengio Samy Bengio and Pascal Vincent. 2009. The difficulty of training deep architectures and the effect of unsupervised pre-training. In Artificial Intelligence and Statistics. PMLR 153--160.  Dumitru Erhan Pierre-Antoine Manzagol Yoshua Bengio Samy Bengio and Pascal Vincent. 2009. The difficulty of training deep architectures and the effect of unsupervised pre-training. In Artificial Intelligence and Statistics. PMLR 153--160."},{"key":"e_1_3_2_2_8_1","volume-title":"International conference on machine learning. PMLR, 1180--1189","author":"Ganin Yaroslav","year":"2015","unstructured":"Yaroslav Ganin and Victor Lempitsky . 2015 . Unsupervised domain adaptation by backpropagation . In International conference on machine learning. PMLR, 1180--1189 . Yaroslav Ganin and Victor Lempitsky. 2015. Unsupervised domain adaptation by backpropagation. In International conference on machine learning. PMLR, 1180--1189."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467086"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271684"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1611835114"},{"key":"e_1_3_2_2_12_1","unstructured":"Cheng Li Yue Lu Qiaozhu Mei Dong Wang and Sandeep Pandey. 2015. Click-through prediction for advertising in twitter timeline. In KDD. 1959--1968.  Cheng Li Yue Lu Qiaozhu Mei Dong Wang and Sandeep Pandey. 2015. Click-through prediction for advertising in twitter timeline. In KDD. 1959--1968."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371793"},{"key":"e_1_3_2_2_14_1","volume-title":"Deep cross networks with aesthetic preference for cross-domain recommendation. arXiv preprint arXiv:1905.13030","author":"Liu Jian","year":"2019","unstructured":"Jian Liu , Pengpeng Zhao , Yanchi Liu , Victor S Sheng , Fuzheng Zhuang , Jiajie Xu , Xiaofang Zhou , and Hui Xiong . 2019. Deep cross networks with aesthetic preference for cross-domain recommendation. arXiv preprint arXiv:1905.13030 ( 2019 ). Jian Liu, Pengpeng Zhao, Yanchi Liu, Victor S Sheng, Fuzheng Zhuang, Jiajie Xu, Xiaofang Zhou, and Hui Xiong. 2019. Deep cross networks with aesthetic preference for cross-domain recommendation. arXiv preprint arXiv:1905.13030 (2019)."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219828"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330677"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330666"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412744"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/369"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3481941"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"crossref","unstructured":"Fei Sun Jun Liu Jian Wu Changhua Pei Xiao Lin Wenwu Ou and Peng Jiang. 2019. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In CIKM. 1441--1450.  Fei Sun Jun Liu Jian Wu Changhua Pei Xiao Lin Wenwu Ou and Peng Jiang. 2019. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In CIKM. 1441--1450.","DOI":"10.1145\/3357384.3357895"},{"key":"e_1_3_2_2_22_1","volume-title":"PTUM: Pre-training User Model from Unlabeled User Behaviors via Self-supervision. arXiv preprint arXiv:2010.01494","author":"Wu Chuhan","year":"2020","unstructured":"Chuhan Wu , Fangzhao Wu , Tao Qi , Jianxun Lian , Yongfeng Huang , and Xing Xie . 2020 . PTUM: Pre-training User Model from Unlabeled User Behaviors via Self-supervision. arXiv preprint arXiv:2010.01494 (2020). Chuhan Wu, Fangzhao Wu, Tao Qi, Jianxun Lian, Yongfeng Huang, and Xing Xie. 2020. PTUM: Pre-training User Model from Unlabeled User Behaviors via Self-supervision. arXiv preprint arXiv:2010.01494 (2020)."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512082"},{"key":"e_1_3_2_2_24_1","volume-title":"Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction. KDD","author":"Wu Kailun","year":"2022","unstructured":"Kailun Wu , Zhangming Chan , Weijie Bian , Lejian Ren , Shiming Xiang , Shuguang Han , Hongbo Deng , and Bo Zheng . 2022a. Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction. KDD ( 2022 ). Kailun Wu, Zhangming Chan, Weijie Bian, Lejian Ren, Shiming Xiang, Shuguang Han, Hongbo Deng, and Bo Zheng. 2022a. Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction. KDD (2022)."},{"key":"e_1_3_2_2_25_1","volume-title":"UPRec: User-Aware Pre-training for Recommender Systems. arXiv preprint arXiv:2102.10989","author":"Xiao Chaojun","year":"2021","unstructured":"Chaojun Xiao , Ruobing Xie , Yuan Yao , Zhiyuan Liu , Maosong Sun , Xu Zhang , and Leyu Lin . 2021. UPRec: User-Aware Pre-training for Recommender Systems. arXiv preprint arXiv:2102.10989 ( 2021 ). Chaojun Xiao, Ruobing Xie, Yuan Yao, Zhiyuan Liu, Maosong Sun, Xu Zhang, and Leyu Lin. 2021. UPRec: User-Aware Pre-training for Recommender Systems. arXiv preprint arXiv:2102.10989 (2021)."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401156"},{"key":"e_1_3_2_2_27_1","volume-title":"DARec: Deep domain adaptation for cross-domain recommendation via transferring rating patterns. arXiv preprint arXiv:1905.10760","author":"Yuan Feng","year":"2019","unstructured":"Feng Yuan , Lina Yao , and Boualem Benatallah . 2019. DARec: Deep domain adaptation for cross-domain recommendation via transferring rating patterns. arXiv preprint arXiv:1905.10760 ( 2019 ). Feng Yuan, Lina Yao, and Boualem Benatallah. 2019. DARec: Deep domain adaptation for cross-domain recommendation via transferring rating patterns. arXiv preprint arXiv:1905.10760 (2019)."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462908"},{"key":"e_1_3_2_2_29_1","volume-title":"PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems. In 2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE.","author":"Zhang Yuanxing","year":"2022","unstructured":"Yuanxing Zhang , Langshi Chen , Siran Yang , Man Yuan , Huimin Yi , 2022 . PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems. In 2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE. Yuanxing Zhang, Langshi Chen, Siran Yang, Man Yuan, Huimin Yi, et al. 2022. PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems. In 2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE."},{"key":"e_1_3_2_2_30_1","volume-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1479--1488","author":"Zhang Yang","year":"2020","unstructured":"Yang Zhang , Fuli Feng , Chenxu Wang , Xiangnan He , Meng Wang , Yan Li , and Yongdong Zhang . 2020 . How to retrain recommender system? A sequential meta-learning method . In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1479--1488 . Yang Zhang, Fuli Feng, Chenxu Wang, Xiangnan He, Meng Wang, Yan Li, and Yongdong Zhang. 2020. How to retrain recommender system? A sequential meta-learning method. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1479--1488."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015941"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219823"},{"key":"e_1_3_2_2_33_1","volume-title":"Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji-Rong Wen.","author":"Zhou Kun","year":"2020","unstructured":"Kun Zhou , Hui Wang , Wayne Xin Zhao , Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji-Rong Wen. 2020 . S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization. In CIKM. 1893--1902. Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji-Rong Wen. 2020. S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization. In CIKM. 1893--1902."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"crossref","unstructured":"Han Zhu Junqi Jin Chang Tan Fei Pan Yifan Zeng Han Li and Kun Gai. 2017. Optimized Cost per Click in Taobao Display Advertising. In KDD. 2191--2200.  Han Zhu Junqi Jin Chang Tan Fei Pan Yifan Zeng Han Li and Kun Gai. 2017. Optimized Cost per Click in Taobao Display Advertising. In KDD. 2191--2200.","DOI":"10.1145\/3097983.3098134"},{"key":"e_1_3_2_2_35_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. 1079--1088.  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. 1079--1088.","DOI":"10.1145\/3219819.3219826"}],"event":{"name":"CIKM '22: The 31st ACM International Conference on Information and Knowledge Management","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Atlanta GA USA","acronym":"CIKM '22"},"container-title":["Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557106","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3511808.3557106","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:30:56Z","timestamp":1750188656000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557106"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,17]]},"references-count":35,"alternative-id":["10.1145\/3511808.3557106","10.1145\/3511808"],"URL":"https:\/\/doi.org\/10.1145\/3511808.3557106","relation":{},"subject":[],"published":{"date-parts":[[2022,10,17]]},"assertion":[{"value":"2022-10-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}