Recommendations as Treatments: Debiasing Learning and Evaluation

17 February 2016

Papers citing "Recommendations as Treatments: Debiasing Learning and Evaluation"

50 / 108 papers shown

Title
Uncertainty Quantification for Fairness in Two-Stage Recommender Systems Lequn Wang Thorsten Joachims 30 22 0 30 May 2022
Cross Pairwise Ranking for Unbiased Item Recommendation Qi Wan Xiangnan He Xiang Wang Jiancan Wu Wei Guo Ruiming Tang CML 32 37 0 26 Apr 2022
ESCM $^2$ : Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation Hao Wang Tai-Wei Chang Tianqiao Liu Jia-Bin Huang Zhichao Chen Chao-hui Yu Ruopeng Li Wei Chu 38 89 0 03 Apr 2022
KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems Chongming Gao Shijun Li Wenqiang Lei Jiawei Chen Biao Li Peng Jiang Xiangnan He Jiaxin Mao Tat-Seng Chua 37 134 0 22 Feb 2022
Partial Identification with Noisy Covariates: A Robust Optimization Approach Wenshuo Guo Mingzhang Yin Yixin Wang Michael I. Jordan 38 19 0 22 Feb 2022
Generalized Strategic Classification and the Case of Aligned Incentives Sagi Levanon Nir Rosenfeld 29 26 0 09 Feb 2022
REST: Debiased Social Recommendation via Reconstructing Exposure Strategies Ruichu Cai Fengzhu Wu Zijian Li Jie Qiao Wei Chen Yuexing Hao Hao Gu CML OffRL 28 5 0 13 Jan 2022
On Sampling Collaborative Filtering Datasets Noveen Sachdeva Carole-Jean Wu Julian McAuley 34 16 0 13 Jan 2022
Deep Causal Reasoning for Recommendations Yaochen Zhu Jing Yi Jiayi Xie Zhenzhong Chen CML BDL 32 10 0 06 Jan 2022
Deep Treatment-Adaptive Network for Causal Inference Qian Li Zhichao Wang Shaowu Liu Gang Li Guandong Xu CML BDL OOD 30 10 0 27 Dec 2021
Obtaining Calibrated Probabilities with Personalized Ranking Models Wonbin Kweon SeongKu Kang Hwanjo Yu FedML 11 15 0 09 Dec 2021
Contextual Bandit Applications in Customer Support Bot Sandra Sajeev Jade Huang Nikos Karampatziakis Matthew Hall Sebastian Kochman Weizhu Chen 30 10 0 06 Dec 2021
Identifiable Generative Models for Missing Not at Random Data Imputation Chao Ma Cheng Zhang 36 34 0 27 Oct 2021
Rethinking Neural vs. Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives Zida Cheng Chuanwei Ruan Siheng Chen Sushant Kumar Ya Zhang 27 16 0 23 Oct 2021
Two-stage Voice Application Recommender System for Unhandled Utterances in Intelligent Personal Assistant Wei Xiao Qian Hu Thahir Mohamed Zheng Gao Xibin Gao Radhika Arava M. Abdelhady 30 3 0 19 Oct 2021
Deconfounded Causal Collaborative Filtering Shuyuan Xu Juntao Tan Shelby Heinecke Jia Li Yongfeng Zhang CML 40 40 0 14 Oct 2021
An Adaptive Boosting Technique to Mitigate Popularity Bias in Recommender System A. Gangwar Shweta Jain FaML 31 4 0 13 Sep 2021
Top-N Recommendation with Counterfactual User Preference Simulation Mengyue Yang Quanyu Dai Zhenhua Dong Xu Chen Xiuqiang He Jun Wang CML BDL 50 65 0 02 Sep 2021
Debiased Explainable Pairwise Ranking from Implicit Feedback Khalil Damak Sami Khenissi O. Nasraoui 29 16 0 30 Jul 2021
On component interactions in two-stage recommender systems Jiri Hron K. Krauth Michael I. Jordan Niki Kilbertus CML LRM 42 31 0 28 Jun 2021
Correcting Exposure Bias for Link Recommendation Shantanu Gupta Hao Wang Zachary Chase Lipton Bernie Wang CML 39 34 0 13 Jun 2021
Matrix Completion with Model-free Weighting Jiayi Wang R. K. Wong Xiaojun Mao Kwun Chuen Gary Chan 34 5 0 09 Jun 2021
AutoDebias: Learning to Debias for Recommendation Jiawei Chen Hande Dong Yang Qiu Xiangnan He Xin Xin Liang Chen Guli Lin Keping Yang CML 43 200 0 10 May 2021
Policy Learning with Adaptively Collected Data Ruohan Zhan Zhimei Ren Susan Athey Zhengyuan Zhou OffRL 45 27 0 05 May 2021
Causal Learning for Socially Responsible AI Lu Cheng Ahmadreza Mosallanezhad Paras Sheth Huan Liu 85 13 0 25 Apr 2021
Multi-Source Causal Inference Using Control Variates Wenshuo Guo S. Wang Peng Ding Yixin Wang Michael I. Jordan CML 55 18 0 30 Mar 2021
Understanding the role of importance weighting for deep learning Da Xu Yuting Ye Chuanwei Ruan FAtt 39 43 0 28 Mar 2021
MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset Chuizheng Meng Loc Trinh Nan Xu Yan Liu 24 30 0 12 Feb 2021
Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions Yanbo Xu Divyat Mahajan Liz Manrao Amit Sharma Emre Kıcıman CML 15 2 0 11 Nov 2020
BLOB : A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals Otmane Sakhi Stephen Bonner D. Rohde Flavian Vasile 30 34 0 28 Aug 2020
Theoretical Modeling of the Iterative Properties of User Discovery in a Collaborative Filtering Recommender System Sami Khenissi M. Boujelbene O. Nasraoui 25 23 0 21 Aug 2020
Unbiased Learning for the Causal Effect of Recommendation Masahiro Sato S. Takemori Janmajay Singh Tomoko Ohkuma CML OffRL 17 69 0 11 Aug 2020
Concept Drift Detection: Dealing with MissingValues via Fuzzy Distance Estimations Anjin Liu Jie Lu Guangquan Zhang 19 15 0 09 Aug 2020
Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions James McInerney B. Brost Praveen Chandar Rishabh Mehrotra Ben Carterette BDL CML OffRL 121 55 0 25 Jul 2020
not-MIWAE: Deep Generative Modelling with Missing not at Random Data Niels Bruun Ipsen Pierre-Alexandre Mattei J. Frellsen DRL 19 54 0 23 Jun 2020
Fairness-Aware Explainable Recommendation over Knowledge Graphs Zuohui Fu Yikun Xian Ruoyuan Gao Jieyu Zhao Qiaoying Huang ... Shuyuan Xu Shijie Geng C. Shah Yongfeng Zhang Gerard de Melo FaML 12 204 0 03 Jun 2020
Controlling Fairness and Bias in Dynamic Learning-to-Rank Marco Morik Ashudeep Singh Jessica Hong Thorsten Joachims 36 206 0 29 May 2020
Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems Chang Zhou Jianxin Ma Jianwei Zhang Jingren Zhou Hongxia Yang 38 142 0 20 May 2020
Non-Clicks Mean Irrelevant? Propensity Ratio Scoring As a Correction Nan Wang Zhen Qin Xuanhui Wang Hongning Wang CML 37 27 0 18 May 2020
Counterfactual Evaluation of Treatment Assignment Functions with Networked Observational Data Ruocheng Guo Jundong Li Huan Liu CML OffRL 36 21 0 22 Dec 2019
Less Is Better: Unweighted Data Subsampling via Influence Function Zifeng Wang Hong Zhu Zhenhua Dong Xiuqiang He Shao-Lun Huang TDI 34 51 0 03 Dec 2019
Missing Not at Random in Matrix Completion: The Effectiveness of Estimating Missingness Probabilities Under a Low Nuclear Norm Assumption Wei-Ying Ma George H. Chen 23 49 0 28 Oct 2019
Large-scale Causal Approaches to Debiasing Post-click Conversion Rate Estimation with Multi-task Learning Wenhao Zhang Wentian Bao Xiao-Yang Liu Keping Yang Quan Lin Hong Wen Ramin Ramezani CML 29 104 0 16 Oct 2019
Affordable Uplift: Supervised Randomization in Controlled Experiments Johannes Haupt D. Jacob R. M. Gubela Stefan Lessmann 37 5 0 01 Oct 2019
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 370 4,237 0 23 Aug 2019
Lessons from Contextual Bandit Learning in a Customer Support Bot Nikos Karampatziakis Sebastian Kochman Jade Huang Paul Mineiro Kathy Osborne Weizhu Chen 21 6 0 06 May 2019
Fairness in Recommendation Ranking through Pairwise Comparisons Alex Beutel Jilin Chen Tulsee Doshi Hai Qian Li Wei ... Lukasz Heldt Zhe Zhao Lichan Hong Ed H. Chi Cristos Goodrow FaML 36 373 0 02 Mar 2019
Matrix Completion under Low-Rank Missing Mechanism Xiaojun Mao Raymond K. W. Wong Songxi Chen 25 15 0 19 Dec 2018
Beyond the Selected Completely At Random Assumption for Learning from Positive and Unlabeled Data Jessa Bekker Pieter Robberechts Jesse Davis 24 83 0 10 Sep 2018
Learning from Positive and Unlabeled Data under the Selected At Random Assumption Jessa Bekker Jesse Davis OOD 16 15 0 27 Aug 2018