{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:07:35Z","timestamp":1765544855607,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":40,"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\/3511808.3557090","type":"proceedings-article","created":{"date-parts":[[2022,10,16]],"date-time":"2022-10-16T01:29:57Z","timestamp":1665883797000},"page":"2984-2993","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Numerical Feature Representation with Hybrid<i>N<\/i>-ary Encoding"],"prefix":"10.1145","author":[{"given":"Bo","family":"Chen","sequence":"first","affiliation":[{"name":"Huawei Noah's Ark Lab, Shenzhen, China"}]},{"given":"Huifeng","family":"Guo","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":"Yue","family":"Ding","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"given":"Yunzhe","family":"Li","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"given":"Wei","family":"Guo","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Shenzhen, China"}]},{"given":"Yichao","family":"Wang","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Shenzhen, China"}]},{"given":"Zhicheng","family":"He","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"}]}],"member":"320","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1198\/000313002146"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"crossref","unstructured":"Bo Chen Yichao Wang Zhirong Liu Ruiming Tang Wei Guo Hongkun Zheng Weiwei Yao Muyu Zhang and Xiuqiang He. 2021. Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models. In CIKM. 3757--3766. Bo Chen Yichao Wang Zhirong Liu Ruiming Tang Wei Guo Hongkun Zheng Weiwei Yao Muyu Zhang and Xiuqiang He. 2021. Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models. In CIKM. 3757--3766.","DOI":"10.1145\/3459637.3481915"},{"key":"e_1_3_2_2_3_1","unstructured":"Ting Chen Simon Kornblith Mohammad Norouzi and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In ICML. PMLR 1597--1607. Ting Chen Simon Kornblith Mohammad Norouzi and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In ICML. PMLR 1597--1607."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2988450.2988454"},{"volume-title":"Numerical notation: A comparative history","author":"Chrisomalis Stephen","key":"e_1_3_2_2_5_1","unstructured":"Stephen Chrisomalis . 2010. Numerical notation: A comparative history . Cambridge University Press . Stephen Chrisomalis. 2010. Numerical notation: A comparative history. Cambridge University Press."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"crossref","unstructured":"Paul Covington Jay Adams and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In RecSys. 191--198. Paul Covington Jay Adams and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In RecSys. 191--198.","DOI":"10.1145\/2959100.2959190"},{"volume-title":"Feature Selection with Decision Tree Criterion","author":"Grabczewski Krzysztof","key":"e_1_3_2_2_7_1","unstructured":"Krzysztof Grabczewski and Norbert Jankowski . 2005. Feature Selection with Decision Tree Criterion . In HIS. IEEE Computer Society , 212--217. Krzysztof Grabczewski and Norbert Jankowski. 2005. Feature Selection with Decision Tree Criterion. In HIS. IEEE Computer Society, 212--217."},{"key":"e_1_3_2_2_8_1","volume-title":"Thomas Borchert, and Ralf Herbrich.","author":"Graepel Thore","year":"2010","unstructured":"Thore Graepel , Joaquin Quinonero Candela , Thomas Borchert, and Ralf Herbrich. 2010 . Web-scale bayesian click-through rate prediction for sponsored search advertising in microsoft's bing search engine. In ICML. 13--20. Thore Graepel, Joaquin Quinonero Candela, Thomas Borchert, and Ralf Herbrich. 2010. Web-scale bayesian click-through rate prediction for sponsored search advertising in microsoft's bing search engine. In ICML. 13--20."},{"key":"e_1_3_2_2_9_1","unstructured":"Yulong Gu Zhuoye Ding Shuaiqiang Wang Lixin Zou Yiding Liu and Dawei Yin. 2020. Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems. In CIKM. 2493--2500. Yulong Gu Zhuoye Ding Shuaiqiang Wang Lixin Zou Yiding Liu and Dawei Yin. 2020. Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems. In CIKM. 2493--2500."},{"key":"e_1_3_2_2_10_1","unstructured":"Huifeng Guo Bo Chen Ruiming Tang Weinan Zhang Zhenguo Li and Xiuqiang He. 2021. An Embedding Learning Framework for Numerical Features in CTR Prediction. In KDD. 2910--2918. Huifeng Guo Bo Chen Ruiming Tang Weinan Zhang Zhenguo Li and Xiuqiang He. 2021. An Embedding Learning Framework for Numerical Features in CTR Prediction. In KDD. 2910--2918."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"crossref","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. 1725--1731. Huifeng Guo Ruiming Tang Yunming Ye Zhenguo Li and Xiuqiang He. 2017. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. In IJCAI. 1725--1731.","DOI":"10.24963\/ijcai.2017\/239"},{"key":"e_1_3_2_2_12_1","volume-title":"AISTATS. JMLR Workshop and Conference Proceedings, 297--304","author":"Gutmann Michael","year":"2010","unstructured":"Michael Gutmann and Aapo Hyv\"arinen. 2010 . Noise-contrastive estimation: A new estimation principle for unnormalized statistical models . In AISTATS. JMLR Workshop and Conference Proceedings, 297--304 . Michael Gutmann and Aapo Hyv\"arinen. 2010. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In AISTATS. JMLR Workshop and Conference Proceedings, 297--304."},{"key":"e_1_3_2_2_13_1","unstructured":"Kaiming He Haoqi Fan Yuxin Wu Saining Xie and Ross Girshick. 2020. Momentum contrast for unsupervised visual representation learning. In CVPR. 9729--9738. Kaiming He Haoqi Fan Yuxin Wu Saining Xie and Ross Girshick. 2020. Momentum contrast for unsupervised visual representation learning. In CVPR. 9729--9738."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2648584.2648589"},{"key":"e_1_3_2_2_15_1","unstructured":"Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In ICML. PMLR 448--456. Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In ICML. PMLR 448--456."},{"key":"e_1_3_2_2_16_1","volume-title":"Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144","author":"Jang Eric","year":"2016","unstructured":"Eric Jang , Shixiang Gu , and Ben Poole . 2016. Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144 ( 2016 ). Eric Jang, Shixiang Gu, and Ben Poole. 2016. Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144 (2016)."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"crossref","unstructured":"Manas R Joglekar Cong Li Mei Chen Taibai Xu Xiaoming Wang Jay K Adams Pranav Khaitan Jiahui Liu and Quoc V Le. 2020. Neural input search for large scale recommendation models. In KDD. 2387--2397. Manas R Joglekar Cong Li Mei Chen Taibai Xu Xiaoming Wang Jay K Adams Pranav Khaitan Jiahui Liu and Quoc V Le. 2020. Neural input search for large scale recommendation models. In KDD. 2387--2397.","DOI":"10.1145\/3394486.3403288"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"crossref","unstructured":"Yu-Chin Juan Yong Zhuang Wei-Sheng Chin and Chih-Jen Lin. 2016. Field-aware Factorization Machines for CTR Prediction. In RecSys. 43--50. Yu-Chin Juan Yong Zhuang Wei-Sheng Chin and Chih-Jen Lin. 2016. Field-aware Factorization Machines for CTR Prediction. In RecSys. 43--50.","DOI":"10.1145\/2959100.2959134"},{"key":"e_1_3_2_2_19_1","unstructured":"Guolin Ke Zhenhui Xu Jia Zhang Jiang Bian and Tie-Yan Liu. 2019. DeepGBM: A Deep Learning Framework Distilled by GBDT for Online Prediction Tasks. In KDD. ACM 384--394. Guolin Ke Zhenhui Xu Jia Zhang Jiang Bian and Tie-Yan Liu. 2019. DeepGBM: A Deep Learning Framework Distilled by GBDT for Online Prediction Tasks. In KDD. ACM 384--394."},{"key":"e_1_3_2_2_20_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_2_21_1","volume-title":"H Chi","author":"Ma Jiaqi","year":"2018","unstructured":"Jiaqi Ma , Zhe Zhao , Xinyang Yi , Jilin Chen , Lichan Hong , and Ed H Chi . 2018 . Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In KDD. 1930--1939. Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi. 2018. Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In KDD. 1930--1939."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"crossref","unstructured":"H Brendan McMahan Gary Holt David Sculley Michael Young Dietmar Ebner Julian Grady Lan Nie Todd Phillips Eugene Davydov Daniel Golovin etal 2013. Ad click prediction: a view from the trenches. In KDD. 1222--1230. H Brendan McMahan Gary Holt David Sculley Michael Young Dietmar Ebner Julian Grady Lan Nie Todd Phillips Eugene Davydov Daniel Golovin et al. 2013. Ad click prediction: a view from the trenches. In KDD. 1222--1230.","DOI":"10.1145\/2487575.2488200"},{"key":"e_1_3_2_2_23_1","unstructured":"Maxim Naumov Dheevatsa Mudigere Hao-Jun Michael Shi Jianyu Huang Narayanan Sundaraman Jongsoo Park Xiaodong Wang Udit Gupta Carole-Jean Wu Alisson G. Azzolini Dmytro Dzhulgakov Andrey Mallevich Ilia Cherniavskii Yinghai Lu Raghuraman Krishnamoorthi Ansha Yu Volodymyr Kondratenko Stephanie Pereira Xianjie Chen Wenlin Chen Vijay Rao Bill Jia Liang Xiong and Misha Smelyanskiy. 2019. Deep Learning Recommendation Model for Personalization and Recommendation Systems. CoRR Vol. abs\/1906.00091 (2019). Maxim Naumov Dheevatsa Mudigere Hao-Jun Michael Shi Jianyu Huang Narayanan Sundaraman Jongsoo Park Xiaodong Wang Udit Gupta Carole-Jean Wu Alisson G. Azzolini Dmytro Dzhulgakov Andrey Mallevich Ilia Cherniavskii Yinghai Lu Raghuraman Krishnamoorthi Ansha Yu Volodymyr Kondratenko Stephanie Pereira Xianjie Chen Wenlin Chen Vijay Rao Bill Jia Liang Xiong and Misha Smelyanskiy. 2019. Deep Learning Recommendation Model for Personalization and Recommendation Systems. CoRR Vol. abs\/1906.00091 (2019)."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3233770"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"crossref","unstructured":"Kan Ren Weinan Zhang Yifei Rong Haifeng Zhang Yong Yu and Jun Wang. 2016. User response learning for directly optimizing campaign performance in display advertising. In CIKM. 679--688. Kan Ren Weinan Zhang Yifei Rong Haifeng Zhang Yong Yu and Jun Wang. 2016. User response learning for directly optimizing campaign performance in display advertising. In CIKM. 679--688.","DOI":"10.1145\/2983323.2983347"},{"volume-title":"Factorization machines","author":"Rendle Steffen","key":"e_1_3_2_2_26_1","unstructured":"Steffen Rendle . 2010. Factorization machines . In ICDM. IEEE , 995--1000. Steffen Rendle. 2010. Factorization machines. In ICDM. IEEE, 995--1000."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/T-C.1970.223044"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"crossref","unstructured":"Matthew Richardson Ewa Dominowska and Robert Ragno. 2007. Predicting clicks: estimating the click-through rate for new ads. In WWW. 521--530. Matthew Richardson Ewa Dominowska and Robert Ragno. 2007. Predicting clicks: estimating the click-through rate for new ads. In WWW. 521--530.","DOI":"10.1145\/1242572.1242643"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357925"},{"key":"e_1_3_2_2_30_1","volume-title":"Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research","author":"Srivastava Nitish","year":"2014","unstructured":"Nitish Srivastava , Geoffrey Hinton , Alex Krizhevsky , Ilya Sutskever , and Ruslan Salakhutdinov . 2014. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research , Vol. 15 , 1 ( 2014 ), 1929--1958. Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, Vol. 15, 1 (2014), 1929--1958."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16561"},{"key":"e_1_3_2_2_32_1","article-title":"Visualizing data using t-SNE","volume":"9","author":"der Maaten Laurens Van","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton . 2008 . Visualizing data using t-SNE . Journal of machine learning research , Vol. 9 , 11 (2008). Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research, Vol. 9, 11 (2008).","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_2_33_1","first-page":"1","article-title":"Deep & Cross Network for Ad Click Predictions","volume":"12","author":"Wang Ruoxi","year":"2017","unstructured":"Ruoxi Wang , Bin Fu , Gang Fu , and Mingliang Wang . 2017 . Deep & Cross Network for Ad Click Predictions . In ADKDD. ACM , 12 : 1 -- 12 :7. Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. 2017. Deep & Cross Network for Ad Click Predictions. In ADKDD. ACM, 12:1--12:7.","journal-title":"ADKDD. ACM"},{"key":"e_1_3_2_2_34_1","volume-title":"Chi","author":"Wang Ruoxi","year":"2021","unstructured":"Ruoxi Wang , Rakesh Shivanna , Derek Cheng , Sagar Jain , Dong Lin , Lichan Hong , and Ed Chi . 2021 . DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems. In WWW. 1785--1797. Ruoxi Wang, Rakesh Shivanna, Derek Cheng, Sagar Jain, Dong Lin, Lichan Hong, and Ed Chi. 2021. DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems. In WWW. 1785--1797."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"crossref","unstructured":"Jiancan Wu Xiang Wang Fuli Feng Xiangnan He Liang Chen Jianxun Lian and Xing Xie. 2021. Self-supervised graph learning for recommendation. In SIGIR. 726--735. Jiancan Wu Xiang Wang Fuli Feng Xiangnan He Liang Chen Jianxun Lian and Xing Xie. 2021. Self-supervised graph learning for recommendation. In SIGIR. 726--735.","DOI":"10.1145\/3404835.3462862"},{"key":"e_1_3_2_2_36_1","first-page":"3926","article-title":"CFM: Convolutional Factorization Machines for Context-Aware Recommendation","volume":"19","author":"Xin Xin","year":"2019","unstructured":"Xin Xin , Bo Chen , Xiangnan He , Dong Wang , Yue Ding , and Joemon Jose . 2019 . CFM: Convolutional Factorization Machines for Context-Aware Recommendation . In IJCAI , Vol. 19. 3926 -- 3932 . Xin Xin, Bo Chen, Xiangnan He, Dong Wang, Yue Ding, and Joemon Jose. 2019. CFM: Convolutional Factorization Machines for Context-Aware Recommendation. In IJCAI, Vol. 19. 3926--3932.","journal-title":"IJCAI"},{"key":"e_1_3_2_2_37_1","volume-title":"Felix Yu, Ting Chen, Aditya Menon, Lichan Hong, Ed H Chi, Steve Tjoa, Jieqi Kang, et al.","author":"Yao Tiansheng","year":"2020","unstructured":"Tiansheng Yao , Xinyang Yi , Derek Zhiyuan Cheng , Felix Yu, Ting Chen, Aditya Menon, Lichan Hong, Ed H Chi, Steve Tjoa, Jieqi Kang, et al. 2020 . Self-supervised Learning for Large-scale Item Recommendations . arXiv preprint arXiv:2007.12865 (2020). Tiansheng Yao, Xinyang Yi, Derek Zhiyuan Cheng, Felix Yu, Ting Chen, Aditya Menon, Lichan Hong, Ed H Chi, Steve Tjoa, Jieqi Kang, et al. 2020. Self-supervised Learning for Large-scale Item Recommendations. arXiv preprint arXiv:2007.12865 (2020)."},{"volume-title":"Deep learning over multi-field categorical data","author":"Zhang Weinan","key":"e_1_3_2_2_38_1","unstructured":"Weinan Zhang , Tianming Du , and Jun Wang . 2016. Deep learning over multi-field categorical data . In ECIR. Springer , 45--57. Weinan Zhang, Tianming Du, and Jun Wang. 2016. Deep learning over multi-field categorical data. In ECIR. Springer, 45--57."},{"key":"e_1_3_2_2_39_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. 4695--4703. Weinan Zhang Jiarui Qin Wei Guo Ruiming Tang and Xiuqiang He. 2021. Deep Learning for Click-Through Rate Estimation. In IJCAI. 4695--4703.","DOI":"10.24963\/ijcai.2021\/636"},{"key":"e_1_3_2_2_40_1","volume-title":"AIM: Automatic Interaction Machine for Click-Through Rate Prediction. TKDE","author":"Zhu Chenxu","year":"2021","unstructured":"Chenxu Zhu , Bo Chen , Weinan Zhang , Jincai Lai , Ruiming Tang , Xiuqiang He , Zhenguo Li , and Yong Yu . 2021 . AIM: Automatic Interaction Machine for Click-Through Rate Prediction. TKDE (2021). Chenxu Zhu, Bo Chen, Weinan Zhang, Jincai Lai, Ruiming Tang, Xiuqiang He, Zhenguo Li, and Yong Yu. 2021. AIM: Automatic Interaction Machine for Click-Through Rate Prediction. TKDE (2021)."}],"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\/3511808.3557090","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3511808.3557090","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\/3511808.3557090"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,17]]},"references-count":40,"alternative-id":["10.1145\/3511808.3557090","10.1145\/3511808"],"URL":"https:\/\/doi.org\/10.1145\/3511808.3557090","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"}}]}}