| Transfer Learning in Collaborative Recommendation for Bias Reduction |
Universal |
TJR |
Recsys 2021 |
Tensorflow |
| AutoDebias: Learning to Debias for Recommendation |
Universal |
AutoDebias |
SIGIR 2021 |
Pytorch |
| A General Knowledge Distillation Framework for Counterfactual Recommendation via Uniform Data |
Universal |
KDCRec |
SIGIR 2020 |
Tensorflow |
| Causal Embeddings for Recommendation |
Universal |
CausE |
Recsys 2018 |
Tensorflow |
|
|
|
|
|
| Combating Selection Biases in Recommender Systems with a Few Unbiased Ratings |
Selection Bias |
LTD |
WSDM 2021 |
|
| Asymmetric tri-training for debiasing missing-not-at-random explicit feedback |
Selection Bias |
AT |
SIGIR 2020 |
|
| Recommendations as treatments: Debiasing learning and evaluation |
Selection Bias |
MF-IPS |
SIGIR 2020 |
Python |
| Doubly robust joint learning for recommendation on data missing not at random |
Selection Bias |
DR |
ICML 2019 |
|
| The deconfounded recommender: A causal inference approach to recommendation |
Selection Bias |
Deconfounded model |
arXiv 2018 |
|
| Social recommendation with missing not at random data |
Selection Bias |
SPMF-MNAR |
ICDM 2018 |
|
| Recommendations as treatments: Debiasing learning and evaluation |
Selection Bias |
MF-IPS |
PMLR 2016 |
Python |
| Boosting Response Aware Model-Based Collaborative Filtering |
Selection Bias |
RAPMF |
TKDE 2015 |
- |
| Probabilistic matrix factorization with non-random missing data |
Selection Bias |
MF-MNAR |
JMLR 2014 |
Python |
| Bayesian Binomial Mixture Model for Collaborative Prediction With Non-Random Missing Data |
Selection Bias |
|
RecSys 2014 |
|
| Evaluation of recommendations: rating-prediction and ranking |
Selection Bias |
|
RecSys 2013 |
|
| Training and testing of recommender systems on data missing not at random |
Selection Bias |
|
KDD 2010 |
|
| Training and testing of recommender systems on data missing not at random |
Selection Bias |
|
KDD 2010 |
|
| Collaborative prediction and ranking with non-random missing data |
Selection Bias |
MM |
RecSys 2009 |
|
| Collaborative filtering and the missing at random assumption |
Selection Bias |
|
UAI 2007 |
Python |
|
|
|
|
|
| Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation |
Conformity Bias |
TIDE |
Arxiv 2021 |
To publish |
| Disentangling user interest and Conformity for recommendation with causal embedding |
Conformity Bias |
DICE |
WWW 2021 |
Pytorch |
| Learning personalized preference of strong and weak ties for social recommendation |
Conformity Bias |
PTPMF |
WWW 2017 |
|
| Are you influenced by others when rating?: Improve rating prediction by conformity modeling |
Conformity Bias |
|
RecSys 2016 |
|
| Xgboost: A scalable tree boosting system |
Conformity Bias |
XGBoost |
KDD 2016 |
Python |
| A probabilistic model for using social networks in personalized item recommendation |
Conformity Bias |
SPF |
RecSys 2015 |
Python |
| mtrust: discerning multi-faceted trust in a connected world |
Conformity Bias |
mTrust |
WSDM 2012 |
|
| Learning to recommend with social trust ensemble |
Conformity Bias |
RSTE |
SIGIR 2009 |
|
|
|
|
|
|
| Non-Clicks Mean Irrelevant? Propensity Ratio Scoring As a Correction |
Exposure Bias |
PRS |
WSDM 2021 |
- |
| Propensity-Independent Bias Recovery in Offline Learning-to-Rank Systems |
Exposure Bias |
LTR |
SIGIR 2021 |
- |
| Mitigating Confounding Bias in Recommendation via Information Bottleneck |
Exposure Bias |
DIB |
Recsys 2021 |
To come |
| Debiased Explainable Pairwise Ranking from Implicit Feedback |
Exposure Bias |
EBPR |
Recsys 2021 |
Pytorch |
| Top-N Recommendation with Counterfactual User Preference Simulation |
Exposure Bias |
CPR |
CIKM 2021 |
- |
| SamWalker++: recommendation with informative sampling strategy |
Exposure Bias |
SamWalker++ |
TKDE 2021 |
Pytorch |
| Deconfounded Causal Collaborative Filtering |
Exposure Bias |
DCCF |
Arxiv 2021 |
- |
| Unbiased recommender learning from missing-not-at-random implicit feedback |
Exposure Bias |
Rel-MF |
WSDM 2020 |
Python |
| Lightgcn: Simplifying and powering graph convolution network for recommendation (LightGCN) |
Exposure Bias |
LightGCN |
SIGIR 2020 |
Python |
| Reinforced negative sampling over knowledge graph for recommendation |
Exposure Bias |
KGPolicy |
WWW 2020 |
Python |
| Fast adaptively weighted matrix factorization for recommendation with implicit feedback |
Exposure Bias |
FAWMF |
AAAI 2020 |
|
| Correcting for selection bias in learning-to-rank systems |
Exposure Bias |
Heckman rank/Propensity SVM rank |
WWW 2020 |
Python |
| Large-scale causal approaches to debiasing post-click conversion rate estimation with multi-task learning |
Exposure Bias |
Multi-IPW/Multi-DR |
WWW 2020 |
|
| Entire space multi-task modeling via post-click behavior decomposition for conversion rate prediction |
Exposure Bias |
ESM^2^ |
SIGIR 2020 |
|
| Gmcm: Graph-based micro-behavior conversion model for post-click conversion rate estimation |
Exposure Bias |
GMCM |
SIGIR 2020 |
|
| ” click” is not equal to” like”: Counterfactual recommendation for mitigating clickbait issue |
Exposure Bias |
CR |
arXiv 2020 |
|
| Unbiased Implicit Recommendation and Propensity Estimation via Combinational Joint Learning |
Exposure Bias |
CJ |
Recsys 2020 |
Tensorflow |
| Debiasing Item-to-Item Recommendations With Small Annotated Datasets |
Exposure Bias |
- |
Recsys 2020 |
- |
| Reinforced negative sampling for recommendation with exposure data |
Exposure Bias |
RNS |
IJCAI 2019 |
Python |
| Samwalker: Social recommendation with informative sampling strategy |
Exposure Bias |
Samwalker |
WWW 2019 |
MATLAB/C++ |
| Samwalker: Social recommendation with informative sampling strategy |
Exposure Bias |
Samwalker |
WWW 2019 |
MATLAB/C++ |
| Collaborative filtering with social exposure: A modular approach to social recommendation |
Exposure Bias |
SERec |
AAAI 2018 |
C++ |
| An improved sampler for bayesian personalized ranking by leveraging view data |
Exposure Bias |
BPR+view |
WWW 2018 |
|
| Unbiased offline recommender evaluation for missing-not-at-random implicit feedback |
Exposure Bias |
|
RecSys 2018 |
Python |
| Entire space multi-task model: An effective approach for estimating post-click conversion rate |
Exposure Bias |
ESMM |
SIGIR 2018 |
|
| “Modeling users’ exposure with social knowledge influence and consumption influence for recommendation |
Exposure Bias |
SoEXBMF |
CIKM 2018 |
|
| Selection of negative samples for one-class matrix factorization |
Exposure Bias |
Full |
SDM 2017 |
Python |
| Selection of negative samples for one-class matrix factorization |
Exposure Bias |
Full |
SDM 2017 |
Python |
| Neural collaborative filtering (NCF) |
Exposure Bias |
NCF |
WWW 2017 |
Python |
| Learning to rank with selection bias in personal search |
Exposure Bias |
Global/Segmented/ Generalized Bias Model |
SIGIR 2016 |
|
| Modeling user exposure in recommendation |
Exposure Bias |
Content ExpoMF/Location ExpoMF |
WWW 2016 |
Python |
| Collaborative denoising auto-encoders for top-n recommender systems (CDAE) |
Exposure Bias |
CDAE |
WSDM 2016 |
C++ |
| Fast matrix factorization for online recommendation with implicit feedback |
Exposure Bias |
eALS |
SIGIR 2016 |
Python |
| Dynamic matrix factorization with priors on unknown values |
Exposure Bias |
|
KDD 2015 |
C++ |
| Logistic matrix factorization for implicit feedback data |
Exposure Bias |
LogisticMF |
NIPS 2014 |
|
| Improving one-class collaborative filtering by incorporating rich user information |
Exposure Bias |
|
CIKM 2010 |
|
| Bpr: Bayesian personalized ranking from implicit feedback |
Exposure Bias |
BPR |
UAI 2009 |
Python |
| Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering |
Exposure Bias |
GALS/sMMMF |
KDD 2009 |
|
| Collaborative filtering for implicit feedback datasets |
Exposure Bias |
|
ICDM 2008 |
Python |
| One-class collaborative filtering |
Exposure Bias |
wALS/sALS |
ICDM 2008 |
|
|
|
|
|
|
| A Graph-Enhanced Click Model for Web Search |
Position Bias |
GraphCM |
SIGIR 2021 |
Pytorch |
| Adapting Interactional Observation Embedding for Counterfactual Learning to Rank |
Position Bias |
IOBM |
SIGIR 2021 |
Tensorflow |
| When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to Rank |
Position Bias |
Affine estimator |
CIKM 2020 |
Tensorflow |
| A deep recurrent survival model for unbiased ranking |
Position Bias |
DRSR |
SIGIR 2020 |
Python |
| Attribute-based propensity for unbiased learning in recommender systems: Algorithm and case studies |
Position Bias |
|
KDD 2020 |
|
| Debiasing grid-based product search in e-commerce |
Position Bias |
|
KDD 2020 |
|
| Cascade model-based propensity estimation for counterfactual learning to rank |
Position Bias |
CM-IPS |
SIGIR 2020 |
Python |
| Addressing Trust Bias for Unbiased Learning-to-Rank |
Position Bias |
TrustPBM |
WWW 2019 |
- |
| Position bias estimation for unbiased learning to rank in personal search |
Position Bias |
Regression-based EM |
WSDM 2018 |
|
| Unbiased learning to rank with unbiased propensity estimation |
Position Bias |
DLA |
SIGIR 2018 |
Python |
| Unbiased learning-to-rank with biased feedback |
Position Bias |
Propensity SVM-Rank |
WSDM 2017 |
Link |
| Multileave gradient descent for fast online learning to rank |
Position Bias |
|
WSDM 2016 |
|
| Learning to rank with selection bias in personal search |
Position Bias |
Global/Segmented/ Generalized Bias Model |
SIGIR 2016 |
|
| Batch learning from logged bandit feedback through counterfactual risk minimization |
Position Bias |
POEM |
JMLR 2015 |
|
| Learning socially optimal information systems from egoistic users |
Position Bias |
SoPer-R/SoPer-S |
ECML PKDD 2013 |
|
| Reusing historical interaction data for faster online learning to rank for ir |
Position Bias |
RHC/CPS |
WSDM 2013 |
Link |
| A novel click model and its applications to online advertising |
Position Bias |
GCM |
WSDM 2010 |
|
| A dynamic bayesian network click model for web search ranking |
Position Bias |
DBN |
WWW 2009 |
|
| Click chain model in web search |
Position Bias |
CCM |
WWW 2009 |
|
| A user browsing model to predict search engine click data from past observations. |
Position Bias |
|
SIGIR 2008 |
|
| An experimental comparison of click position-bias models |
Position Bias |
|
WSDM 2008 |
|
| Comparing click logs and editorial labels for training query rewriting |
Position Bias |
|
WWW 2007 |
|
|
|
|
|
|
| Popularity bias in ranking and recommendation |
Popularity Bias |
|
AIES 2019 |
|
| Disentangling User Interest and Conformity for Recommendation with Causal Embedding |
Popularity Bias |
DICE |
WWW 2021 |
Python |
| The Unfairness of Popularity Bias in Recommendation |
Popularity Bias |
VAE-CF |
SAC 2021 |
Pytorch |
| Popularity Bias in Dynamic Recommendation |
Popularity Bias |
FPC |
KDD 2021 |
Tensorflow |
| Causal Intervention for Leveraging Popularity Bias in Recommendation |
Popularity Bias |
PDA |
SIGIR 2021 |
Tensorflow |
| Deconfounded Recommendation for Alleviating Bias Amplification |
Popularity Bias |
DecRS |
KDD 2021 |
Pytorch |
| Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation |
Popularity Bias |
TIDE |
Arxiv 2021 |
To come |
| Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system |
Popularity Bias |
MACR |
KDD 2021 |
Tensorflow |
| ESAM: discriminative domain adaptation with non-displayed items to improve long-tail performance |
Popularity Bias |
ESAM |
SIGIR 2020 |
Python |
| Unbiased offline recommender evaluation for missing-not-at-random implicit feedback |
Popularity Bias |
|
RecSys 2018 |
Python |
| An adversarial approach to improve long-tail performance in neural collaborative filtering |
Popularity Bias |
|
CIKM 2018 |
|
| Controlling popularity bias in learning-to-rank recommendation |
Popularity Bias |
|
RecSys 2017 |
|
| Incorporating diversity in a learning to rank recommender system |
Popularity Bias |
|
FLAIRS 2016 |
|
| The limits of popularity-based recommendations, and the role of social ties |
Popularity Bias |
|
KDD 2016 |
C++ |
| Correcting popularity bias by enhancing recommendation neutrality |
Popularity Bias |
|
RecSys 2014 |
|
| Efficiency improvement of neutrality-enhanced recommendation |
Popularity Bias |
|
RecSys 2013 |
Link |
|
|
|
|
|
| Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Users |
Unfairness |
NAECF |
WSDM 2021 |
Python |
| User-oriented Fairness in Recommendation |
Unfairness |
|
WWW2021 |
Python |
| Policy-Gradient Training of Fair and Unbiased Ranking Functions |
Unfairness |
FULTR |
SIGIR 2021 |
Python |
| Towards Long-term Fairness in Recommendation |
Unfairness |
FCPO |
WSDM 2021 |
Python |
| Towards Personalized Fairness based on Causal Notion |
Unfairness |
|
SIGIR 2021 |
|
| Learning Fair Representations for Recommendation: A Graph-based Perspective |
Unfairness |
FairGo |
WWW 2021 |
PyTorch |
| Debiasing Career Recommendations with Neural Fair Collaborative Filtering |
Unfairness |
NFCF |
WWW 2021 |
PyTorch |
| Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness |
Unfairness |
PL-Rank |
SIGIR 2021 |
Tensorflow |
| Debayes: a bayesian method for debiasing network embeddings |
Unfairness |
DeBayes |
ICML 2020 |
Link |
| Measuring and Mitigating Item Under-Recommendation Bias in Personalized Ranking Systems |
Unfairness |
DPR |
SIGIR 2020 |
Tensorflow |
| Controlling fairness and bias in dynamic learning-to-rank |
Unfairness |
FairCo |
SIGIR 2020 |
Python |
| Designing fair ranking schemes |
Unfairness |
|
SIGMOD 2019 |
|
| Fairwalk: Towards fair graph embedding |
Unfairness |
Fairwalk |
IJCAI 2019 |
|
| Fairness in recommendation ranking through pairwise comparisons |
Unfairness |
|
KDD 2019 |
|
| “Compositional fairness constraints for graph embeddings |
Unfairness |
|
ICML 2019 |
Python |
| Fairness-aware ranking in search & recommendation systems with application to linkedin talent search |
Unfairness |
PSL |
KDD 2019 |
|
| Counterfactual fairness: Unidentification bound and algorithm. |
Unfairness |
|
IJCAI 2019 |
|
| Privacy-aware recommendation with private-attribute protection using adversarial learning |
Unfairness |
RAP |
WSDM 2019 |
|
| Policy Learning for Fairness in Ranking |
Unfairness |
FAIR-PG-RANK |
NIPS 2019 |
Python |
| Fairness of exposure in rankings |
Unfairness |
|
KDD 2018 |
|
| Fairness-aware tensor-based recommendation |
Unfairness |
FATR |
CIKM 2018 |
Pyhton |
| Fairness in decision-making - the causal explanation formula |
Unfairness |
Ctf-DE/Ctf-IE/Ctf-SE |
AAAI 2018 |
|
| On discrimination discovery and removal in ranked data using causal graph |
Unfairness |
FRank/FDetect |
KDD 2018 |
|
| A fairness-aware hybrid recommender system |
Unfairness |
PSL |
FATREC 2018 |
|
| Fair inference on outcomes |
Unfairness |
|
AAAI 2018 |
C++ |
| Equity of attention: Amortizing individual fairness in rankings |
Unfairness |
|
SIGIR 2018 |
|
| Fa*ir: A fair top-k ranking algorithm |
Unfairness |
FA*IR |
CIKM 2017 |
Java/Python |
| Beyond parity: Fairness objectives for collaborative filtering |
Unfairness |
|
NIPS 2017 |
|
| Balanced neighborhoods for fairness-aware collaborative recommendation |
Unfairness |
SLIM |
RecSys 2017 |
|
| Controlling popularity bias in learning-to-rank recommendation |
Unfairness |
|
RecSys 2017 |
|
| Considerations on recommendation independence for a find-good-items task |
Unfairness |
IERS |
Recsys 2017 |
|
| New fairness metrics for recommendation that embrace differences |
Unfairness |
|
FAT/ML 2017 |
|
| Fairness-aware group recommendation with pareto-efficiency |
Unfairness |
|
RecSys 2017 |
|
| Counterfactual fairness |
Unfairness |
counterfactual fairness |
arXiv 2017 |
Python |
| Censoring representations with an adversary |
Unfairness |
ALFR |
ICLR 2016 |
|
| Model-based approaches for independence-enhanced recommendation |
Unfairness |
|
IEEE 2016 |
Link |
| Efficiency improvement of neutrality-enhanced recommendation. |
Unfairness |
|
RecSys 2013 |
Link |
| Learning fair representations |
Unfairness |
|
JMLR 2013 |
|
| Enhancement of the neutrality in recommendation |
Unfairness |
|
RecSys 2012 |
|
| Discrimination-aware data mining |
Unfairness |
|
KDD 2008 |
|
|
|
|
|
|
| AutoDebias: Learning to Debias for Recommendation |
Loop Effect |
AutoDebias |
SIGIR 2021 |
PyTorch |
| A general knowledge distillation framework for counterfactual recommendation via uniform data |
Loop Effect |
KDCRec |
SIGIR 2020 |
Python |
| Influence function for unbiased recommendation |
Loop Effect |
IF4URec |
SIGIR 2020 |
|
| Jointly learning to recommend and advertise |
Loop Effect |
RAM |
KDD 2020 |
|
| Counterfactual evaluation of slate recommendations with sequential reward interactions |
Loop Effect |
RIPS |
KDD 2020 |
Python |
| Joint policy value learning for recommendation |
Loop Effect |
Dual Bandit |
KDD 2020 |
Python |
| Degenerate feedback loops in recommender systems |
Loop Effect |
Oracle |
AIES 2019 |
|
| When people change their mind: Off-policy evaluation in non-stationary recommendation environments |
Loop Effect |
|
WSDM 2019 |
Python |
| Top-k off-policy correction for a reinforce recommender system |
Loop Effect |
REINFORCE |
WSDM 2019 |
Python |
| Improving ad click prediction by considering non-displayed events |
Loop Effect |
|
CIKM 2019 |
Link |
| Large-scale interactive recommendation with tree-structured policy gradient |
Loop Effect |
TPGR |
AAAI 2019 |
Python |
| Deep reinforcement learning for list-wise recommendations |
Loop Effect |
LIRD |
KDD 2019 |
Python |
| Debiasing the human-recommender system feedback loop in collaborative filtering |
Loop Effect |
propensity MF |
WWW 2019 |
|
| Causal embeddings for recommendation |
Loop Effect |
CausE |
RecSys 2018 |
Python |
| Stabilizing reinforcement learning in dynamic environment with application to online recommendation |
Loop Effect |
Robust DQN |
KDD 2018 |
|
| Recommendations with negative feedback via pairwise deep reinforcement learning |
Loop Effect |
DEERS |
KDD 2018 |
|
| Drn: A deep reinforcement learning framework for news recommendation |
Loop Effect |
DDQN |
WWW 2018 |
|
| Deep reinforcement learning for page-wise recommendations |
Loop Effect |
DeepPage |
RecSys 2018 |
|
| A reinforcement learning framework for explainable recommendation |
Loop Effect |
|
ICDM 2018 |
|
| Interactive social recommendation |
Loop Effect |
ISR |
CIKM 2017 |
|
| Off-policy evaluation for slate recommendation |
Loop Effect |
PI |
NIPS 2017 |
Python |
| Factorization bandits for interactive recommendation. |
Loop Effect |
FactorUCB |
AAAI 2017 |
|
| Predicting counterfactuals from large historical data and small randomized trials |
Loop Effect |
|
WWW 2016 |
|
| Deconvolving feedbackloops in recommender systems |
Loop Effect |
Deconvolving feedback |
NIPS 2016 |
|
| Interactive collaborative filtering |
Loop Effect |
ICF |
CIKM 2013 |
Python |
| A contextual-bandit approach to personalized news article recommendation |
Loop Effect |
LinUCB |
WWW 2010 |
Python |