{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T21:25:32Z","timestamp":1660339532608},"publisher-location":"New York, NY, USA","reference-count":47,"publisher":"ACM","funder":[{"name":"National Natural Science Foundation of China","award":["61702327, 61772333, 61632017"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7,25]]},"DOI":"10.1145\/3397271.3401073","type":"proceedings-article","created":{"date-parts":[[2020,7,25]],"date-time":"2020-07-25T07:50:08Z","timestamp":1595663408000},"source":"Crossref","is-referenced-by-count":5,"title":["A Deep Recurrent Survival Model for Unbiased Ranking"],"prefix":"10.1145","author":[{"given":"Jiarui","family":"Jin","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"given":"Yuchen","family":"Fang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"given":"Weinan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"given":"Kan","family":"Ren","sequence":"additional","affiliation":[{"name":"Microsoft Research, Shanghai, China"}]},{"given":"Guorui","family":"Zhou","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}]},{"given":"Jian","family":"Xu","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}]},{"given":"Yong","family":"Yu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"University College London, London, United Kingdom"}]},{"given":"Xiaoqiang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}]},{"given":"Kun","family":"Gai","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2020,7,25]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"crossref","unstructured":"Aman Agarwal Kenta Takatsu Ivan Zaitsev and Thorsten Joachims. 2019. A General Framework for Counterfactual Learning-to-Rank. In SIGIR. Aman Agarwal Kenta Takatsu Ivan Zaitsev and Thorsten Joachims. 2019. A General Framework for Counterfactual Learning-to-Rank. In SIGIR.","DOI":"10.1145\/3331184.3331202"},{"key":"e_1_3_2_2_2_1","volume-title":"Unbiased Learning to Rank with Unbiased Propensity Estimation. SIGIR","author":"Ai Qingyao","year":"2018","unstructured":"Qingyao Ai , Keping Bi , Cheng Luo , Jiafeng Guo , and W Bruce Croft . 2018. Unbiased Learning to Rank with Unbiased Propensity Estimation. SIGIR ( 2018 ). Qingyao Ai, Keping Bi, Cheng Luo, Jiafeng Guo, and W Bruce Croft. 2018. Unbiased Learning to Rank with Unbiased Propensity Estimation. SIGIR (2018)."},{"key":"e_1_3_2_2_3_1","unstructured":"Qingyao Ai Jiaxin Mao Yiqun Liu and W Bruce Croft. 2018. Unbiased learning to rank: Theory and practice. In CIKM. Qingyao Ai Jiaxin Mao Yiqun Liu and W Bruce Croft. 2018. Unbiased learning to rank: Theory and practice. In CIKM."},{"key":"e_1_3_2_2_4_1","unstructured":"Ahmed M Alaa and Mihaela van der Schaar. 2017. Deep multi-task gaussian processes for survival analysis with competing risks. In NeurIPS. Ahmed M Alaa and Mihaela van der Schaar. 2017. Deep multi-task gaussian processes for survival analysis with competing risks. In NeurIPS."},{"key":"e_1_3_2_2_5_1","unstructured":"Per K Andersen Ornulf Borgan Richard D Gill and Niels Keiding. 2012. Statistical models based on counting processes. Per K Andersen Ornulf Borgan Richard D Gill and Niels Keiding. 2012. Statistical models based on counting processes."},{"key":"e_1_3_2_2_6_1","volume-title":"Proceedings of the Learning to Rank Challenge.","author":"Chapelle Olivier","year":"2011","unstructured":"Olivier Chapelle and Yi Chang . 2011 . Yahoo! learning to rank challenge overview . In Proceedings of the Learning to Rank Challenge. Olivier Chapelle and Yi Chang. 2011. Yahoo! learning to rank challenge overview. In Proceedings of the Learning to Rank Challenge."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"crossref","unstructured":"Olivier Chapelle and Ya Zhang. 2009. A dynamic bayesian network click model for web search ranking. In WWW. Olivier Chapelle and Ya Zhang. 2009. A dynamic bayesian network click model for web search ranking. In WWW.","DOI":"10.1145\/1526709.1526711"},{"key":"e_1_3_2_2_8_1","volume-title":"Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological)","author":"Cox David R","year":"1972","unstructured":"David R Cox . 1972. Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological) ( 1972 ). David R Cox. 1972. Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological) (1972)."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"crossref","unstructured":"Nick Craswell Onno Zoeter Michael Taylor and Bill Ramsey. 2008. An experimental comparison of click position-bias models. In WSDM. Nick Craswell Onno Zoeter Michael Taylor and Bill Ramsey. 2008. An experimental comparison of click position-bias models. In WSDM.","DOI":"10.1145\/1341531.1341545"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"crossref","unstructured":"Georges E Dupret and Benjamin Piwowarski. 2008. A user browsing model to predict search engine click data from past observations.. In SIGIR. Georges E Dupret and Benjamin Piwowarski. 2008. A user browsing model to predict search engine click data from past observations.. In SIGIR.","DOI":"10.1145\/1390334.1390392"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-19274-7_47"},{"key":"e_1_3_2_2_12_1","volume-title":"Intervention harvesting for context-dependent examination-bias estimation. SIGIR","author":"Fang Zhichong","year":"2018","unstructured":"Zhichong Fang , Aman Agarwal , and Thorsten Joachims . 2018. Intervention harvesting for context-dependent examination-bias estimation. SIGIR ( 2018 ). Zhichong Fang, Aman Agarwal, and Thorsten Joachims. 2018. Intervention harvesting for context-dependent examination-bias estimation. SIGIR (2018)."},{"key":"e_1_3_2_2_13_1","volume-title":"Tree-structured survival analysis. Cancer treatment reports","author":"Gordon Louis","year":"1985","unstructured":"Louis Gordon and Richard A Olshen . 1985. Tree-structured survival analysis. Cancer treatment reports ( 1985 ). Louis Gordon and Richard A Olshen. 1985. Tree-structured survival analysis. Cancer treatment reports (1985)."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"crossref","unstructured":"Fan Guo Chao Liu Anitha Kannan Tom Minka Michael Taylor Yi-Min Wang and Christos Faloutsos. 2009. Click chain model in web search. In WWW. Fan Guo Chao Liu Anitha Kannan Tom Minka Michael Taylor Yi-Min Wang and Christos Faloutsos. 2009. Click chain model in web search. In WWW.","DOI":"10.1145\/1526709.1526712"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"crossref","unstructured":"Fan Guo Chao Liu and Yi Min Wang. 2009. Efficient multiple-click models in web search. In WSDM. Fan Guo Chao Liu and Yi Min Wang. 2009. Efficient multiple-click models in web search. In WSDM.","DOI":"10.1145\/1498759.1498818"},{"key":"e_1_3_2_2_16_1","volume-title":"Long short-term memory. Neural computation","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber . 1997. Long short-term memory. Neural computation ( 1997 ). Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation (1997)."},{"key":"e_1_3_2_2_17_1","unstructured":"Ziniu Hu Yang Wang Qu Peng and Hang Li. 2019. Unbiased LambdaMART: An Unbiased Pairwise Learning-to-Rank Algorithm. In WWW. Ziniu Hu Yang Wang Qu Peng and Hang Li. 2019. Unbiased LambdaMART: An Unbiased Pairwise Learning-to-Rank Algorithm. In WWW."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"crossref","unstructured":"Rolf Jagerman Harrie Oosterhuis and Maarten de Rijke. 2019. To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions. (2019). Rolf Jagerman Harrie Oosterhuis and Maarten de Rijke. 2019. To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions. (2019).","DOI":"10.1145\/3331184.3331269"},{"key":"e_1_3_2_2_19_1","volume":"201","author":"Jing How","unstructured":"How Jing and Alexander J Smola. 201 7. Neural survival recommender. In WSDM. How Jing and Alexander J Smola. 2017. Neural survival recommender. In WSDM.","journal-title":"Alexander J Smola."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"crossref","unstructured":"Thorsten Joachims Laura A Granka Bing Pan Helene Hembrooke and Geri Gay. 2005. Accurately interpreting click through data as implicit feedback. In SIGIR. Thorsten Joachims Laura A Granka Bing Pan Helene Hembrooke and Geri Gay. 2005. Accurately interpreting click through data as implicit feedback. In SIGIR.","DOI":"10.1145\/1076034.1076063"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"crossref","unstructured":"Thorsten Joachims Adith Swaminathan and Tobias Schnabel. 2017. Unbiased learning-to-rank with biased feedback. In WSDM. Thorsten Joachims Adith Swaminathan and Tobias Schnabel. 2017. Unbiased learning-to-rank with biased feedback. In WSDM.","DOI":"10.1145\/3018661.3018699"},{"key":"e_1_3_2_2_22_1","volume-title":"Nonparametric estimation from incomplete observations. Journal of the American statistical association","author":"Kaplan Edward L","year":"1958","unstructured":"Edward L Kaplan and Paul Meier . 1958. Nonparametric estimation from incomplete observations. Journal of the American statistical association ( 1958 ). Edward L Kaplan and Paul Meier. 1958. Nonparametric estimation from incomplete observations. Journal of the American statistical association (1958)."},{"key":"e_1_3_2_2_23_1","volume-title":"Deep Surv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC medical research methodology","author":"Katzman Jared L","year":"2018","unstructured":"Jared L Katzman , Uri Shaham , Alexander Cloninger , Jonathan Bates , Tingting Jiang , and Yuval Kluger . 2018. Deep Surv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC medical research methodology ( 2018 ). Jared L Katzman, Uri Shaham, Alexander Cloninger, Jonathan Bates, Tingting Jiang, and Yuval Kluger. 2018. Deep Surv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC medical research methodology (2018)."},{"key":"e_1_3_2_2_24_1","volume-title":"Lightgbm: A highly efficient gradient boosting decision tree. In NeurIPS.","author":"Ke Guolin","year":"2017","unstructured":"Guolin Ke , Qi Meng , Thomas Finley , Taifeng Wang , Wei Chen , Weidong Ma , Qiwei Ye , and Tie-Yan Liu . 2017 . Lightgbm: A highly efficient gradient boosting decision tree. In NeurIPS. Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. Lightgbm: A highly efficient gradient boosting decision tree. In NeurIPS."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"crossref","unstructured":"Faisal M Khan and Valentina Bayer Zubek. 2008. Support vector regression for censored data (SVRc): a novel tool for survival analysis. In ICDM. Faisal M Khan and Valentina Bayer Zubek. 2008. Support vector regression for censored data (SVRc): a novel tool for survival analysis. In ICDM.","DOI":"10.1109\/ICDM.2008.50"},{"key":"e_1_3_2_2_26_1","volume-title":"Statistical methods for survival data analysis","author":"Lee Elisa T","unstructured":"Elisa T Lee and John Wang . 2003. Statistical methods for survival data analysis . Vol. 476 . John Wiley & Sons . Elisa T Lee and John Wang. 2003. Statistical methods for survival data analysis. Vol. 476. John Wiley & Sons."},{"key":"e_1_3_2_2_27_1","unstructured":"Jane Li Scott Huffman and Akihito Tokuda. 2009. Good abandonment in mobile and PC internet search. In SIGIR. Jane Li Scott Huffman and Akihito Tokuda. 2009. Good abandonment in mobile and PC internet search. In SIGIR."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"crossref","unstructured":"Yan Li Jie Wang Jieping Ye and Chandan K Reddy. 2016. A multi-task learning formulation for survival analysis. In KDD. Yan Li Jie Wang Jieping Ye and Chandan K Reddy. 2016. A multi-task learning formulation for survival analysis. In KDD.","DOI":"10.1145\/2939672.2939857"},{"key":"e_1_3_2_2_29_1","unstructured":"Tie-Yan Liu etal 2009. Learning to rank for information retrieval. Foundations and Trends\u00ae in Information Retrieval (2009). Tie-Yan Liu et al. 2009. Learning to rank for information retrieval. Foundations and Trends\u00ae in Information Retrieval (2009)."},{"key":"e_1_3_2_2_30_1","volume-title":"Deep survival analysis. arXiv","author":"Ranganath Rajesh","year":"2016","unstructured":"Rajesh Ranganath , Adler Perotte , No\u00e9mie Elhadad , and David Blei . 2016. Deep survival analysis. arXiv ( 2016 ). Rajesh Ranganath, Adler Perotte, No\u00e9mie Elhadad, and David Blei. 2016. Deep survival analysis. arXiv (2016)."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"crossref","unstructured":"Kan Ren Yuchen Fang Weinan Zhang Shuhao Liu Jiajun Li Ya Zhang Yong Yu and Jun Wang. 2018. Learning multi-touch conversion attribution with dual-attention mechanisms for online advertising. In CIKM. Kan Ren Yuchen Fang Weinan Zhang Shuhao Liu Jiajun Li Ya Zhang Yong Yu and Jun Wang. 2018. Learning multi-touch conversion attribution with dual-attention mechanisms for online advertising. In CIKM.","DOI":"10.1145\/3269206.3271677"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"crossref","unstructured":"Kan Ren Jiarui Qin Lei Zheng Zhengyu Yang Weinan Zhang Lin Qiu and Yong Yu. 2019. Deep recurrent survival analysis. In AAAI. Kan Ren Jiarui Qin Lei Zheng Zhengyu Yang Weinan Zhang Lin Qiu and Yong Yu. 2019. Deep recurrent survival analysis. In AAAI.","DOI":"10.1609\/aaai.v33i01.33014798"},{"key":"e_1_3_2_2_33_1","volume-title":"Deep Landscape Forecasting for Real-time Bidding Advertising. KDD","author":"Ren Kan","year":"2019","unstructured":"Kan Ren , Jiarui Qin , Lei Zheng , Weinan Zhang , and Yong Yu. 2019. Deep Landscape Forecasting for Real-time Bidding Advertising. KDD ( 2019 ). Kan Ren, Jiarui Qin, Lei Zheng, Weinan Zhang, and Yong Yu. 2019. Deep Landscape Forecasting for Real-time Bidding Advertising. KDD (2019)."},{"key":"e_1_3_2_2_34_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. Matthew Richardson Ewa Dominowska and Robert Ragno. 2007. Predicting clicks: estimating the click-through rate for new ads. In WWW.","DOI":"10.1145\/1242572.1242643"},{"key":"e_1_3_2_2_35_1","volume-title":"The central role of the propensity score in observational studies for causal effects. Biometrika","author":"Rosenbaum Paul R","year":"1983","unstructured":"Paul R Rosenbaum and Donald B Rubin . 1983. The central role of the propensity score in observational studies for causal effects. Biometrika ( 1983 ). Paul R Rosenbaum and Donald B Rubin. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika (1983)."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"crossref","unstructured":"Yang Song Xiaolin Shi Ryen White and Ahmed Hassan Awadallah. 2014. Context-aware web search abandonment prediction. In SIGIR. Yang Song Xiaolin Shi Ryen White and Ahmed Hassan Awadallah. 2014. Context-aware web search abandonment prediction. In SIGIR.","DOI":"10.1145\/2600428.2609604"},{"key":"e_1_3_2_2_37_1","volume-title":"The lasso method for variable selection in the Cox model. Statistics in medicine","author":"Tibshirani Robert","year":"1997","unstructured":"Robert Tibshirani . 1997. The lasso method for variable selection in the Cox model. Statistics in medicine ( 1997 ). Robert Tibshirani. 1997. The lasso method for variable selection in the Cox model. Statistics in medicine (1997)."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"crossref","unstructured":"Chao Wang Yiqun Liu Meng Wang Ke Zhou Jian-yun Nie and Shaoping Ma. 2015. Incorporating non-sequential behavior into click models. In SIGIR. Chao Wang Yiqun Liu Meng Wang Ke Zhou Jian-yun Nie and Shaoping Ma. 2015. Incorporating non-sequential behavior into click models. In SIGIR.","DOI":"10.1145\/2766462.2767712"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"crossref","unstructured":"Hongning Wang ChengXiang Zhai Anlei Dong and Yi Chang. 2013. Contentaware click modeling. In WWW. Hongning Wang ChengXiang Zhai Anlei Dong and Yi Chang. 2013. Contentaware click modeling. In WWW.","DOI":"10.1145\/2488388.2488508"},{"key":"e_1_3_2_2_40_1","volume-title":"Arjen P De Vries, and Marcel JT Reinders","author":"Wang Jun","year":"2006","unstructured":"Jun Wang , Arjen P De Vries, and Marcel JT Reinders . 2006 . A user-item relevance model for log-based collaborative filtering. In ECIR. Jun Wang, Arjen P De Vries, and Marcel JT Reinders. 2006. A user-item relevance model for log-based collaborative filtering. In ECIR."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3214306"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"crossref","unstructured":"Xuanhui Wang Michael Bendersky Donald Metzler and Marc Najork. 2016. Learning to rank with selection bias in personal search. In SIGIR. Xuanhui Wang Michael Bendersky Donald Metzler and Marc Najork. 2016. Learning to rank with selection bias in personal search. In SIGIR.","DOI":"10.1145\/2911451.2911537"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"crossref","unstructured":"Xuanhui Wang Nadav Golbandi Michael Bendersky Donald Metzler and Marc Najork. 2018. Position bias estimation for unbiased learning to rank in personal search. In WSDM. Xuanhui Wang Nadav Golbandi Michael Bendersky Donald Metzler and Marc Najork. 2018. Position bias estimation for unbiased learning to rank in personal search. In WSDM.","DOI":"10.1145\/3159652.3159732"},{"key":"e_1_3_2_2_44_1","volume-title":"XLNet: Generalized Auto regressive Pretraining for Language Understanding. arXiv","author":"Yang Zhilin","year":"2019","unstructured":"Zhilin Yang , Zihang Dai , Yiming Yang , Jaime Carbonell , Ruslan Salakhutdinov , and Quoc V Le. 2019. XLNet: Generalized Auto regressive Pretraining for Language Understanding. arXiv ( 2019 ). Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V Le. 2019. XLNet: Generalized Auto regressive Pretraining for Language Understanding. arXiv (2019)."},{"key":"e_1_3_2_2_45_1","unstructured":"Saizheng Zhang Yuhuai Wu Tong Che Zhouhan Lin Roland Memisevic Ruslan R Salakhutdinov and Yoshua Bengio. 2016. Architectural complexity measures of recurrent neural networks. In NeurIPS. Saizheng Zhang Yuhuai Wu Tong Che Zhouhan Lin Roland Memisevic Ruslan R Salakhutdinov and Yoshua Bengio. 2016. Architectural complexity measures of recurrent neural networks. In NeurIPS."},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"crossref","unstructured":"Weinan Zhang Tianxiong Zhou JunWang and Jian Xu. 2016. Bid-aware Gradient Descent for Unbiased Learning with Censored Data in Display Advertising. In KDD. Weinan Zhang Tianxiong Zhou JunWang and Jian Xu. 2016. Bid-aware Gradient Descent for Unbiased Learning with Censored Data in Display Advertising. In KDD.","DOI":"10.1145\/2939672.2939713"},{"key":"e_1_3_2_2_47_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"}],"event":{"name":"SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval","location":"Virtual Event China","acronym":"SIGIR '20","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3397271.3401073","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:51:11Z","timestamp":1660333871000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3397271.3401073"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,25]]},"references-count":47,"alternative-id":["10.1145\/3397271.3401073","10.1145\/3397271"],"URL":"http:\/\/dx.doi.org\/10.1145\/3397271.3401073","relation":{},"published":{"date-parts":[[2020,7,25]]}}}