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Debias Can be Unreliable: Mitigating Bias Issue in Evaluating Debiasing Recommendation
v1v2 (latest)

Debias Can be Unreliable: Mitigating Bias Issue in Evaluating Debiasing Recommendation

7 September 2024
Chengbing Wang
Wentao Shi
Jizhi Zhang
Wenjie Wang
Hang Pan
Fuli Feng
ArXiv (abs)PDFHTMLGithub

Papers citing "Debias Can be Unreliable: Mitigating Bias Issue in Evaluating Debiasing Recommendation"

18 / 18 papers shown
A Survey on Popularity Bias in Recommender Systems
A Survey on Popularity Bias in Recommender Systems
Anastasiia Klimashevskaia
Dietmar Jannach
Mehdi Elahi
C. Trattner
802
125
0
02 Aug 2023
On the Theories Behind Hard Negative Sampling for Recommendation
On the Theories Behind Hard Negative Sampling for RecommendationThe Web Conference (WWW), 2023
Wentao Shi
Jiawei Chen
Fuli Feng
Jizhi Zhang
Junkang Wu
Chongming Gao
Xiangnan He
BDL
460
65
0
07 Feb 2023
Towards Reliable Item Sampling for Recommendation Evaluation
Towards Reliable Item Sampling for Recommendation EvaluationAAAI Conference on Artificial Intelligence (AAAI), 2022
Dong Li
Ruoming Jin
Zhenming Liu
Bin Ren
Jing Gao
Zhi Liu
266
11
0
28 Nov 2022
A Generalized Doubly Robust Learning Framework for Debiasing Post-Click
  Conversion Rate Prediction
A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate PredictionKnowledge Discovery and Data Mining (KDD), 2022
Quanyu Dai
Haoxuan Li
Peng Wu
Zhenhua Dong
Xiao-Hua Zhou
Rui Zhang
Rui zhang
Jie Sun
260
66
0
12 Nov 2022
StableDR: Stabilized Doubly Robust Learning for Recommendation on Data
  Missing Not at Random
StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at RandomInternational Conference on Learning Representations (ICLR), 2022
Haoxuan Li
Chunyuan Zheng
Peng Wu
455
65
0
10 May 2022
KuaiRec: A Fully-observed Dataset and Insights for Evaluating
  Recommender Systems
KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender SystemsInternational Conference on Information and Knowledge Management (CIKM), 2022
Chongming Gao
Shijun Li
Wenqiang Lei
Jiawei Chen
Biao Li
Peng Jiang
Xiangnan He
Jiaxin Mao
Tat-Seng Chua
449
223
0
22 Feb 2022
A Case Study on Sampling Strategies for Evaluating Neural Sequential
  Item Recommendation Models
A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation ModelsACM Conference on Recommender Systems (RecSys), 2021
Alexander Dallmann
Daniel Zoller
Andreas Hotho
222
71
0
27 Jul 2021
AutoDebias: Learning to Debias for Recommendation
AutoDebias: Learning to Debias for RecommendationAnnual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2021
Jiawei Chen
Hande Dong
Yang Qiu
Xiangnan He
Xin Xin
Liang Chen
Guli Lin
Keping Yang
CML
680
245
0
10 May 2021
User-centered Evaluation of Popularity Bias in Recommender Systems
User-centered Evaluation of Popularity Bias in Recommender SystemsUser Modeling, Adaptation, and Personalization (UMAP), 2021
Himan Abdollahpouri
M. Mansoury
Robin Burke
B. Mobasher
E. Malthouse
434
155
0
10 Mar 2021
On Estimating Recommendation Evaluation Metrics under Sampling
On Estimating Recommendation Evaluation Metrics under SamplingAAAI Conference on Artificial Intelligence (AAAI), 2021
R. Jin
Dong Li
Benjamin Mudrak
Jing Gao
Zhi Liu
192
15
0
02 Mar 2021
Information Theoretic Counterfactual Learning from Missing-Not-At-Random
  Feedback
Information Theoretic Counterfactual Learning from Missing-Not-At-Random FeedbackNeural Information Processing Systems (NeurIPS), 2020
Zifeng Wang
Xi Chen
Rui Wen
Shao-Lun Huang
E. Kuruoglu
Yefeng Zheng
BDLCMLOffRL
401
93
0
06 Sep 2020
LightGCN: Simplifying and Powering Graph Convolution Network for
  Recommendation
LightGCN: Simplifying and Powering Graph Convolution Network for RecommendationAnnual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2020
Xiangnan He
Kuan Deng
Xiang Wang
Yan Li
Yongdong Zhang
Meng Wang
GNN
912
5,028
0
06 Feb 2020
The Unfairness of Popularity Bias in Music Recommendation: A
  Reproducibility Study
The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility StudyEuropean Conference on Information Retrieval (ECIR), 2019
Dominik Kowald
Markus Schedl
Elisabeth Lex
293
158
0
10 Dec 2019
Predicting Counterfactuals from Large Historical Data and Small
  Randomized Trials
Predicting Counterfactuals from Large Historical Data and Small Randomized Trials
Nir Rosenfeld
Yishay Mansour
E. Yom-Tov
CML
274
27
0
24 Oct 2016
Recommendations as Treatments: Debiasing Learning and Evaluation
Recommendations as Treatments: Debiasing Learning and Evaluation
Tobias Schnabel
Adith Swaminathan
Ashudeep Singh
Navin Chandak
Thorsten Joachims
CML
536
794
0
17 Feb 2016
Learning From Missing Data Using Selection Bias in Movie Recommendation
Learning From Missing Data Using Selection Bias in Movie Recommendation
Claire Vernade
Olivier Cappé
CML
231
6
0
30 Sep 2015
Collaborative Filtering and the Missing at Random Assumption
Collaborative Filtering and the Missing at Random AssumptionConference on Uncertainty in Artificial Intelligence (UAI), 2007
Benjamin M. Marlin
R. Zemel
S. Roweis
Malcolm Slaney
347
328
0
20 Jun 2012
BPR: Bayesian Personalized Ranking from Implicit Feedback
BPR: Bayesian Personalized Ranking from Implicit FeedbackConference on Uncertainty in Artificial Intelligence (UAI), 2009
Steffen Rendle
Christoph Freudenthaler
Zeno Gantner
Lars Schmidt-Thieme
BDL
595
6,514
0
09 May 2012
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