ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1909.03601
  4. Cited By
Unbiased Recommender Learning from Missing-Not-At-Random Implicit
  Feedback
v1v2v3 (latest)

Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback

9 September 2019
Yuta Saito
Suguru Yaginuma
Yuta Nishino
Hayato Sakata
Kazuhide Nakata
    CML
ArXiv (abs)PDFHTML

Papers citing "Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback"

22 / 72 papers shown
Title
TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased
  Recommendations
TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations
Haoxuan Li
Yan Lyu
Chunyuan Zheng
Peng Wu
109
45
0
19 Mar 2022
A Real-World Implementation of Unbiased Lift-based Bidding System
A Real-World Implementation of Unbiased Lift-based Bidding System
Daisuke Moriwaki
Yuta Hayakawa
Akira Matsui
Yuta Saito
Isshu Munemasa
Masashi Shibata
33
2
0
23 Feb 2022
KuaiRec: A Fully-observed Dataset and Insights for Evaluating
  Recommender Systems
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
100
145
0
22 Feb 2022
Doubly Robust Off-Policy Evaluation for Ranking Policies under the
  Cascade Behavior Model
Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model
Haruka Kiyohara
Yuta Saito
Tatsuya Matsuhiro
Yusuke Narita
N. Shimizu
Yasuo Yamamoto
OffRL
90
43
0
03 Feb 2022
Augment & Valuate : A Data Enhancement Pipeline for Data-Centric AI
Augment & Valuate : A Data Enhancement Pipeline for Data-Centric AI
Youngjune Lee
Oh Joon Kwon
Haejun Lee
Joonyoung Kim
Kangwook Lee
Kee-Eung Kim
39
9
0
07 Dec 2021
Learning Robust Recommender from Noisy Implicit Feedback
Learning Robust Recommender from Noisy Implicit Feedback
Wenjie Wang
Fuli Feng
Xiangnan He
Liqiang Nie
Tat-Seng Chua
NoLa
65
3
0
02 Dec 2021
Rethinking Neural vs. Matrix-Factorization Collaborative Filtering: the
  Theoretical Perspectives
Rethinking Neural vs. Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives
Zida Cheng
Chuanwei Ruan
Siheng Chen
Sushant Kumar
Ya Zhang
84
16
0
23 Oct 2021
Deconfounded Causal Collaborative Filtering
Deconfounded Causal Collaborative Filtering
Shuyuan Xu
Juntao Tan
Shelby Heinecke
Jia Li
Yongfeng Zhang
CML
127
40
0
14 Oct 2021
Recommender systems based on graph embedding techniques: A comprehensive
  review
Recommender systems based on graph embedding techniques: A comprehensive review
Yue Deng
121
25
0
20 Sep 2021
Debiased Explainable Pairwise Ranking from Implicit Feedback
Debiased Explainable Pairwise Ranking from Implicit Feedback
Khalil Damak
Sami Khenissi
O. Nasraoui
71
17
0
30 Jul 2021
Online Evaluation Methods for the Causal Effect of Recommendations
Online Evaluation Methods for the Causal Effect of Recommendations
Masahiro Sato
CML
23
7
0
14 Jul 2021
Enhanced Doubly Robust Learning for Debiasing Post-click Conversion Rate
  Estimation
Enhanced Doubly Robust Learning for Debiasing Post-click Conversion Rate Estimation
Siyuan Guo
Lixin Zou
Yiding Liu
Wenwen Ye
Suqi Cheng
Shuaiqiang Wang
Hechang Chen
D. Yin
Yi-Ju Chang
70
93
0
28 May 2021
Be Causal: De-biasing Social Network Confounding in Recommendation
Be Causal: De-biasing Social Network Confounding in Recommendation
Qian Li
Xiang-jian Wang
Guandong Xu
CML
105
56
0
17 May 2021
AutoDebias: Learning to Debias for Recommendation
AutoDebias: Learning to Debias for Recommendation
Jiawei Chen
Hande Dong
Yang Qiu
Xiangnan He
Xin Xin
Liang Chen
Guli Lin
Keping Yang
CML
138
207
0
10 May 2021
Causal Collaborative Filtering
Causal Collaborative Filtering
Shuyuan Xu
Yingqiang Ge
Yunqi Li
Zuohui Fu
Xu Chen
Yongfeng Zhang
CML
116
48
0
03 Feb 2021
Causality-Aware Neighborhood Methods for Recommender Systems
Causality-Aware Neighborhood Methods for Recommender Systems
Masahiro Sato
S. Takemori
Janmajay Singh
Qian Zhang
BDLCML
26
7
0
17 Dec 2020
Adversarial Counterfactual Learning and Evaluation for Recommender
  System
Adversarial Counterfactual Learning and Evaluation for Recommender System
Da Xu
Chuanwei Ruan
Evren Körpeoglu
Sushant Kumar
Kannan Achan
OffRLCML
56
33
0
08 Nov 2020
Do Offline Metrics Predict Online Performance in Recommender Systems?
Do Offline Metrics Predict Online Performance in Recommender Systems?
K. Krauth
Sarah Dean
Alex Zhao
Wenshuo Guo
Mihaela Curmei
Benjamin Recht
Michael I. Jordan
OffRL
81
41
0
07 Nov 2020
Unbiased Learning for the Causal Effect of Recommendation
Unbiased Learning for the Causal Effect of Recommendation
Masahiro Sato
S. Takemori
Janmajay Singh
Tomoko Ohkuma
CMLOffRL
111
70
0
11 Aug 2020
Unbiased Lift-based Bidding System
Unbiased Lift-based Bidding System
Daisuke Moriwaki
Yuta Hayakawa
Isshu Munemasa
Yuta Saito
Akira Matsui
44
4
0
08 Jul 2020
Towards Resolving Propensity Contradiction in Offline Recommender
  Learning
Towards Resolving Propensity Contradiction in Offline Recommender Learning
Yuta Saito
Masahiro Nomura
OffRL
96
13
0
16 Oct 2019
Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit
  Feedback
Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback
SrinivasaRao SubramanyaRao
119
114
0
08 Sep 2019
Previous
12