jiawei-chen/recdebiasing

This repository collects debiasing methods for recommendation

Recommendation Debiasing

This website collects recent works and datasets on recommendation debiasing and their codes. We hope this website could help you do search on this topic.

Contents

• 1. Survey Papers

• 2. Datasets

• 3. Debiasing Strategies

  • 3.1 Multiply Biases
  • 3.2 Selection Bias
  • 3.3 Conformity Bias
  • 3.4 Exposure Bias
  • 3.5 Position Bias
  • 3.6 Popularity Bias
  • 3.7 Inductive Bias
  • 3.8 Unfairness
  • 3.9 Loop Effect
  • 3.10 Other Bias

• Tips

1. Survey Papers

1. A Survey on the Fairness of Recommender Systems. TOIS 2023. [pdf]

2. Bias and Debias in Recommender System: A Survey and Future Directions. TOIS 2023. [pdf]

3. Bias Issues and Solutions in Recommender System. WWW 2021,Recsys 2021. [pdf]

4. A survey on bias and fairness in machine learning. Arxiv 2019. [pdf]

2. Datasets

We collect some datasets which include unbiased data and are often used in the research of recommendation debiasing.

1. Yahoo!R3: Collaborative Prediction and Ranking with Non-Random Missing Data. Recsys 2009. [pdf][data]

2. Coat: Recommendations as Treatments: Debiasing Learning and Evaluation. ICML 2016. [pdf][data]

3. KuaiRec: A Fully-observed Dataset for Recommender Systems. CIKM 2022. [pdf][data]

4. KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos. CIKM 2022.[pdf][data]

3. Debiasing Strategies

3.1 Multiply Biases

1. Bounding System-Induced Biases in Recommender Systems with a Randomized Dataset. TOIS 2023.[pdf] [code]

2. Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations. WWW 2023.[pdf]

3. Transfer Learning in Collaborative Recommendation for Bias Reduction. Recsys 2021.[pdf] [code]

4. AutoDebias: Learning to Debias for Recommendation. SIGIR 2021.[pdf] [code]

5. A General Knowledge Distillation Framework for Counterfactual Recommendation via Uniform Data. SIGIR 2020.[pdf] [code]

6. Causal Embeddings for Recommendation. Recsys 2018.[pdf] [code]

3.2 Selection Bias

1. Reconsidering Learning Objectives in Unbiased Recommendation A Distribution Shift Perspective. KDD 2023.[pdf]

2. Propensity Matters Measuring and Enhancing Balancing for Recommendation. ICML 2023.[pdf]

3. A Generalized Propensity Learning Framework for Unbiased Post-Click Conversion Rate Estimation. CIKM 2023.[pdf] [code]

4. CDR: Conservative Doubly Robust Learning for Debiased Recommendation. CIKM 2023.[pdf] [code]

5. UKD: Debiasing Conversion Rate Estimation via Uncertainty-regularized Knowledge Distillation. WWW 2022.[pdf]

6. Practical Counterfactual Policy Learning for Top-𝐾 Recommendations. KDD 2022.[pdf]

7. Debiasing Neighbor Aggregation for Graph Neural Network in Recommender Systems. CIKM 2022.[pdf]

8. Representation Matters When Learning From Biased Feedback in Recommendation. CIKM 2022.[pdf]

9. Hard Negatives or False Negatives: Correcting Pooling Bias in Training Neural Ranking Models. CIKM 2022.[pdf]

10. Be Causal: De-biasing Social Network Confounding in Recommendation. TKDD 2022.[pdf]

11. Debiased recommendation with neural stratification. AI OPEN 2022.[pdf]

12. ESCM2: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation. SIGIR 2022.[pdf]

13. Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction. KDD 2022.[pdf]

14. Combating Selection Biases in Recommender Systems with a Few Unbiased Ratings. WSDM 2021.[pdf]

15. Doubly Robust Estimator for Ranking Metrics with Post‐Click Conversions. RecSys 2020.[pdf] [code]

16. Asymmetric tri-training for debiasing missing-not-at-random explicit feedback. SIGIR 2020.[pdf]

17. Recommendations as treatments: Debiasing learning and evaluation. ICML 2016.[pdf] [code]

18. Doubly robust joint learning for recommendation on data missing not at random. ICML 2019.[pdf]

19. The deconfounded recommender: A causal inference approach to recommendation. arXiv 2018.[pdf]

20. Social recommendation with missing not at random data. ICDM 2018.[pdf]

21. Recommendations as treatments: Debiasing learning and evaluation. [pdf]

22. Boosting Response Aware Model-Based Collaborative Filtering. TKDE 2015.[pdf]

23. Probabilistic matrix factorization with non-random missing data. PMLR 2014.[pdf] [code]

24. Bayesian Binomial Mixture Model for Collaborative Prediction With Non-Random Missing Data. RecSys 2014.[pdf]

25. Evaluation of recommendations: rating-prediction and ranking. RecSys 2013.[pdf]

26. Training and testing of recommender systems on data missing not at random. KDD 2010.[pdf]

27. Collaborative prediction and ranking with non-random missing data. RecSys 2009.[pdf]

28. Collaborative filtering and the missing at random assumption. UAI 2007.[pdf] [code]

3.3 Conformity Bias

1. Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation. TKDE 2022.[pdf]

2. Disentangling user interest and Conformity for recommendation with causal embedding. WWW 2021.[pdf] [code]

3. When Sheep Shop: Measuring Herding Effects in Product Ratings with Natural Experiments. WWW 2018.[pdf] [code]

4. Learning personalized preference of strong and weak ties for social recommendation. WWW 2017.[pdf]

5. Are you influenced by others when rating?: Improve rating prediction by conformity modeling. RecSys 2016.[pdf]

6. Xgboost: A scalable tree boosting system. KDD 2016.[pdf] [code]

7. A probabilistic model for using social networks in personalized item recommendation. RecSys 2015.[pdf] [code]

8. Why amazon’s ratings might mislead you: The story of herding effects. Big data 2014.[pdf]

9. A methodology for learning, analyzing, and mitigating social influence bias in recommender systems. RecSys 2014.[pdf] [code]

10. Mtrust: discerning multi-faceted trust in a connected world. WSDM 2012.[pdf]

11. Learning to recommend with social trust ensemble. SIGIR 2009.[pdf]

3.4 Exposure Bias

1. uCTRL Unbiased Contrastive Representation Learning via Alignment and Uniformity for Collaborative Filtering. SIGIR 2023.[pdf] [code]

2. Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss. NIPS 2023.[pdf] [code]

3. Debiasing Sequential Recommenders through Distributionally Robust Optimization over System Exposure. WSDM 2023.[pdf] [code]

4. Debiasing the Cloze Task in Sequential Recommendation with Bidirectional Transformers. KDD 2022.[pdf]

5. Debiasing Neighbor Aggregation for Graph Neural Network in Recommender Systems. CIKM 2022.[pdf]

6. Non-Clicks Mean Irrelevant? Propensity Ratio Scoring As a Correction. WSDM 2021.[pdf]

7. Propensity-Independent Bias Recovery in Offline Learning-to-Rank Systems. SIGIR 2021.[pdf]

8. Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue. SIGIR 2021.[pdf] [code]

9. Mitigating Confounding Bias in Recommendation via Information Bottleneck. Recsys 2021.[pdf] [code]

10. Debiased Explainable Pairwise Ranking from Implicit Feedback. Recsys 2021.[pdf] [code]

11. Top-N Recommendation with Counterfactual User Preference Simulation. CIKM 2021.[pdf]

12. SamWalker++: recommendation with informative sampling strategy. TKDE 2021.[pdf] [code]

13. Deconfounded Causal Collaborative Filtering. Arxiv 2021/TORS 2023.[pdf]

14. Unbiased recommender learning from missing-not-at-random implicit feedback. WSDM 2020.[pdf] [code]

15. Reinforced negative sampling over knowledge graph for recommendation. WWW 2020.[pdf] [code]

16. Fast adaptively weighted matrix factorization for recommendation with implicit feedback. AAAI 2020.[pdf]

17. Correcting for selection bias in learning-to-rank systems. WWW 2020.[pdf] [code]

18. Large-scale causal approaches to debiasing post-click conversion rate estimation with multi-task learning. WWW 2020.[pdf]

19. Entire space multi-task modeling via post-click behavior decomposition for conversion rate prediction. SIGIR 2020.[pdf]

20. A general knowledge distillation framework for counterfactual recommendation via uniform data. SIGIR 2020.[pdf] [code]

21. Unbiased Implicit Recommendation and Propensity Estimation via Combinational Joint Learning. Recsys 2020.[pdf] [code]

22. Debiasing Item-to-Item Recommendations With Small Annotated Datasets. Recsys 2020.[pdf]

23. Reinforced negative sampling for recommendation with exposure data. IJCAI 2019.[pdf] [code]

24. Samwalker: Social recommendation with informative sampling strategy. WWW 2019.[pdf] [code]

25. Collaborative filtering with social exposure: A modular approach to social recommendation. AAAI 2018.[pdf] [code]

26. An improved sampler for bayesian personalized ranking by leveraging view data. WWW 2018.[pdf]

27. Unbiased offline recommender evaluation for missing-not-at-random implicit feedback. RecSys 2018.[pdf] [code]

28. Entire space multi-task model: An effective approach for estimating post-click conversion rate. SIGIR 2018.[pdf]

29. Modeling users’ exposure with social knowledge influence and consumption influence for recommendation. CIKM 2018.[pdf]

30. Selection of negative samples for one-class matrix factorization. SDM 2017.[pdf] [code]

31. Learning to rank with selection bias in personal search. SIGIR 2016.[pdf]

32. Modeling user exposure in recommendation. WWW 2016.[pdf] [code]

33. Collaborative denoising auto-encoders for top-n recommender systems (CDAE). WSDM 2016.[pdf] [code]

34. Fast matrix factorization for online recommendation with implicit feedback. SIGIR 2016.[pdf] [code]

35. Dynamic matrix factorization with priors on unknown values. KDD 2015.[pdf] [code]

36. Logistic matrix factorization for implicit feedback data. NIPS 2014.[pdf]

37. Improving one-class collaborative filtering by incorporating rich user information. CIKM 2010.[pdf]

38. Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering. KDD 2009.[pdf]

39. Collaborative filtering for implicit feedback datasets. ICDM 2008.[pdf] [code]

40. One-class collaborative filtering. ICDM 2008.[pdf]

3.5 Position Bias

1. An Offline Metric for the Debiasedness of Click Models. SIGIR 2023.[pdf] [code]

2. A Probabilistic Position Bias Model for Short-Video Recommendation Feeds. RecSys 2023.[pdf] [code]

3. Unbiased Learning to Rank with Biased Continuous Feedback. CIKM 2022.[pdf] [code]

4. Scalar is Not Enough: Vectorization-based Unbiased Learning to Rank. KDD 2022.[pdf] [code]

5. Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model. WSDM 2022.[pdf] [code]

6. Can Clicks Be Both Labels and Features?: Unbiased Behavior Feature Collection and Uncertainty-aware Learning to Rank. SIGIR 2022.[pdf] [code]

7. A Graph-Enhanced Click Model for Web Search. SIGIR 2021.[pdf] [code]

8. Adapting Interactional Observation Embedding for Counterfactual Learning to Rank. SIGIR 2021.[pdf] [code]

9. When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to Rank. CIKM 2020.[pdf] [code]

10. A deep recurrent survival model for unbiased ranking. SIGIR 2020.[pdf] [code]

11. Attribute-based propensity for unbiased learning in recommender systems: Algorithm and case studies. KDD 2020.[pdf]

12. Debiasing grid-based product search in e-commerce. KDD 2020.[pdf]

13. Cascade model-based propensity estimation for counterfactual learning to rank. SIGIR 2020.[pdf)] [code]

14. Addressing Trust Bias for Unbiased Learning-to-Rank. WWW 2019.[pdf]

15. Position bias estimation for unbiased learning to rank in personal search. WSDM 2018.[pdf]

16. A study of position bias in digital library recommender systems. ArXiv 2018.[pdf]

17. Offline Evaluation of Ranking Policies with Click Models. KDD 2018.[pdf]

18. Unbiased learning to rank with unbiased propensity estimation. SIGIR 2018.[pdf] [code]

19. Unbiased learning-to-rank with biased feedback. WSDM 2017.[pdf] [code]

20. Multileave gradient descent for fast online learning to rank. WSDM 2016.[pdf]

21. Learning to rank with selection bias in personal search. SIGIR 2016.[pdf]

22. Accurately interpreting clickthrough data as implicit feedback. SIGIR 2016.[pdf]

23. Batch learning from logged bandit feedback through counterfactual risk minimization. JMLR 2015.[pdf]

24. Learning socially optimal information systems from egoistic users. ECML PKDD 2013.[pdf]

25. Reusing historical interaction data for faster online learning to rank for ir. WSDM 2013.[pdf] [code]

26. A novel click model and its applications to online advertising. WSDM 2010.[pdf]

27. A dynamic bayesian network click model for web search ranking. WWW 2009.[pdf]

28. Click chain model in web search. WWW 2009.[pdf]

29. A user browsing model to predict search engine click data from past observations. SIGIR 2008.[pdf]

30. An experimental comparison of click position-bias models. WSDM 2008.[pdf]

31. Comparing click logs and editorial labels for training query rewriting. WWW 2007.[pdf]

32. Evaluating the accuracy of implicit feedback from clicks and query reformulations in web search. ACM Journals 2007.[pdf]

33. Modeling result-list searching in the world wide web: The role of relevance topologies and trust bias. CogSci 2006.[pdf]

3.6 Popularity Bias

1. TCCM Time and Content-Aware Causal Model for Unbiased News Recommendation. CIKM 2023.[pdf] [code]

2. Rlieving Popularity Bias in Interactive Recommendation A Diversity-Novelty-Aware Reinforcement Learning Approach. TOIS 2023.[pdf] [code]

3. Test-Time Embedding Normalization for Popularity Bias Mitigation. CIKM 2023.[pdf] [code]

4. Potential Factors Leading to Popularity Unfairness in Recommender Systems A User-Centered Analysis. Arxiv 2023.[pdf]

5. Mitigating the Popularity Bias of Graph Collaborative Filtering A Dimensional Collapse Perspective. NIPS 2023.[pdf] [code]

6. A Model-Agnostic Popularity Debias Training Framework for Click-Through Rate Prediction in Recommender System. SIGIR 2023.[pdf]

7. Popularity Debiasing from Exposure to Interaction in Collaborative Filtering. SIGIR 2023.[pdf] [code]

8. Adaptive Popularity Debiasing Aggregator for Graph Collaborative Filtering. SIGIR 2023.[pdf]

9. HDNR A Hyperbolic-Based Debiased Approach for Personalized News Recommendation. SIGIR 2023.[pdf]

10. MELT Mutual Enhancement of Long-Tailed User and Item for Sequential Recommendation. SIGIR 2023.[pdf] [code]

11. Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random. ICLR 2023.[pdf]

12. Invariant Collaborative Filtering to Popularity Distribution Shift. WWW 2023.[pdf] [code]

13. Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering. SIGIR 2022.[pdf] [code]

14. Evolution of Popularity Bias: Empirical Study and Debiasing. KDD 2022.[pdf] [code]

15. Countering Popularity Bias by Regularizing Score Differences. RecSys 2022.[pdf] [code]

16. Co-training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommenders. SIGIR 2022.[pdf]

17. Popularity bias in ranking and recommendation. AIES 2019.[pdf]

18. Disentangling User Interest and Conformity for Recommendation with Causal Embedding. WWW 2021.[pdf] [code]

19. The Unfairness of Popularity Bias in Recommendation. SAC 2021.[pdf] [code]

20. Popularity Bias in Dynamic Recommendation. KDD 2021.[pdf] [code]

21. Causal Intervention for Leveraging Popularity Bias in Recommendation. SIGIR 2021.[pdf] [code]

22. Deconfounded Recommendation for Alleviating Bias Amplification. KDD 2021.[pdf] [code]

23. Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation. Arxiv 2021/TKDE 2022.[pdf]

24. Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system. KDD 2021.[pdf] [code]

25. Popularity-Opportunity Bias in Collaborative Filtering. WSDM 2021.[pdf]

26. Multi-sided exposure bias in recommendation. Arxiv 2020.[pdf]

27. The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation. RecSys 2020.[pdf]

28. ESAM: discriminative domain adaptation with non-displayed items to improve long-tail performance. SIGIR 2020.[pdf] [code]

29. Unbiased offline recommender evaluation for missing-not-at-random implicit feedback. RecSys 2018.[pdf] [code]

30. An adversarial approach to improve long-tail performance in neural collaborative filtering. CIKM 2018.[pdf]

31. A Probabilistic Reformulation of Memory-Based Collaborative Filtering – Implications on Popularity Biases. SIGIR 2017[pdf]

32. Controlling popularity bias in learning-to-rank recommendation. RecSys 2017.[pdf]

33. Incorporating diversity in a learning to rank recommender system. FLAIRS 2016.[pdf]

34. What recommenders recommend: an analysis of recommendation biases and possible countermeasures. UMUAI 2015.[pdf]

35. The limits of popularity-based recommendations, and the role of social ties. KDD 2016.[pdf] [code]

36. Correcting popularity bias by enhancing recommendation neutrality. RecSys 2014.[pdf]

37. Efficiency improvement of neutrality-enhanced recommendation. RecSys 2013.[pdf] [code]

3.7 Inductive Bias

1. Discrete content-aware matrix factorization. KDD 2017.[pdf]

2. Neural collaborative filtering. WWWW 2017.[pdf] [code]

3. Discrete collaborative filtering. SIGIR 2016.[pdf]

4. Logistic matrix factorization for implicit feedback data. NIPS 2014.[pdf]

5. Learning binary codes for collaborative filtering. KDD 2012.[pdf]

3.8 Unfairness

1. Providing Previously Unseen Users Fair Recommendations Using Variational Autoencoders. RecSys 2023.[pdf] [code]

2. Path-Specific Counterfactual Fairness for Recommender Systems. KDD 2023.[pdf] [code]

3. Towards Robust Fairness-aware Recommendation. Recsys 2023.[pdf]

4. When Fairness meets Bias a Debiased Framework for Fairness aware Top-N Recommendation. Recsys 2023.[pdf]

5. Two-sided Calibration for Quality-aware Responsible Recommendation. Recsys 2023.[pdf] [code]

6. Rectifying Unfairness in Recommendation Feedback Loop. SIGIR 2023.[pdf]

7. Measuring Item Global Residual Value for Fair Recommendation. SIGIR 2023.[pdf] [code]

8. Improving Recommendation Fairness via Data Augmentation. WWW 2023.[pdf] [code]

9. Controllable Universal Fair Representation Learning. WWW 2023.[pdf]

10. Cascaded Debiasing: Studying the Cumulative Effect of Multiple Fairness-Enhancing Interventions. CIKM 2022.[pdf] [code]

11. Fighting Mainstream Bias in Recommender Systems via Local Fine Tuning. WSDM 2022.[pdf] [code]

12. CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems. SIGIR 2022.[pdf] [code]

13. Fairness of Exposure in Light of Incomplete Exposure Estimation. SIGIR 2022.[pdf] [code]

14. Explainable Fairness in Recommendation. SIGIR 2022.[pdf]

15. Joint Multisided Exposure Fairness for Recommendation. SIGIR 2022.[pdf] [code]

16. Pareto-Optimal Fairness-Utility Amortizations in Rankings with a DBN Exposure Model. SIGIR 2022.[pdf] [code]

17. Optimizing generalized Gini indices for fairness in rankings. SIGIR 2022.[pdf]

18. Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking. SIGIR 2022.[pdf] [code]

19. Measuring Fairness in Ranked Outputs. SIGIR 2022.[pdf]

20. Comprehensive Fair Meta-learned Recommender System. KDD 2022.[pdf] [code]

21. Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking. KDD 2022.[pdf] [code]

22. Fair Representation Learning: An Alternative to Mutual Information. KDD 2022.[pdf] [code]

23. Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Users. WSDM 2021.[pdf] [code]

24. User Bias in Beyond-Accuracy Measurement of Recommendation Algorithms. RecSys 2021.[pdf]

25. User-oriented Fairness in Recommendation. WWW2021.[pdf] [code]

26. Policy-Gradient Training of Fair and Unbiased Ranking Functions. SIGIR 2021.[pdf] [code]

27. Towards Long-term Fairness in Recommendation. WSDM 2021.[pdf] [code]

28. Towards Personalized Fairness based on Causal Notion. SIGIR 2021.[pdf]

29. Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness. SIGIR 2021.[pdf] [code]

30. Learning Fair Representations for Recommendation: A Graph-based Perspective. WWW 2021.[pdf] [code]

31. Debiasing Career Recommendations with Neural Fair Collaborative Filtering. WWW 2021.[pdf] [code]

32. Debayes: a bayesian method for debiasing network embeddings. ICML 2020.[pdf] [code]

33. Measuring and Mitigating Item Under-Recommendation Bias in Personalized Ranking Systems. SIGIR 2020.[pdf] [code]

34. Controlling fairness and bias in dynamic learning-to-rank. SIGIR 2020.[pdf] [code]

35. Designing fair ranking schemes. SIGMOD 2019.[pdf]

36. Fairwalk: Towards fair graph embedding. IJCAI 2019.[pdf]

37. Fairness in recommendation ranking through pairwise comparisons. KDD 2019.[pdf]

38. Compositional fairness constraints for graph embeddings. ICML 2019.[pdf] [code]

39. Fairness-aware ranking in search & recommendation systems with application to linkedin talent search. KDD 2019.[pdf]

40. Counterfactual fairness: Unidentification bound and algorithm. IJCAI 2019.[pdf]

41. Privacy-aware recommendation with private-attribute protection using adversarial learning. WSDM 2019.[pdf]

42. Algorithmic bias? an empirical study of apparent gender-based discrimination in the display of stem career ads. INFORMS 2019.[pdf]

43. Crank up the volume: preference bias amplification in collaborative recommendation. RecSys 2019.[pdf]

44. Policy Learning for Fairness in Ranking. NIPS 2019.[pdf] [code]

45. Fairness of exposure in rankings. KDD 2018.[pdf]

46. Fairness-aware tensor-based recommendation. CIKM 2018.[pdf] [code]

47. Fairness in decision-making - the causal explanation formula. AAAI 2018.[pdf]

48. On discrimination discovery and removal in ranked data using causal graph. KDD 2018.[pdf]

49. A fairness-aware hybrid recommender system. FATREC 2018.[pdf]

50. Fair inference on outcomes. AAAI 2018.[pdf] [code]

51. Exploring author gender in book rating and recommendation. RecSys 2018.[pdf] [code]

52. Homophily influences ranking of minorities in social networks. Scientific Reports 2018.[pdf]

53. Algorithmic glass ceiling in social networks: The effects of social recommendations on network diversity. WWW 2018.[pdf]

54. Equity of attention: Amortizing individual fairness in rankings. SIGIR 2018.[pdf]

55. Fa*ir: A fair top-k ranking algorithm. CIKM 2017.[pdf] [code]

56. Beyond parity: Fairness objectives for collaborative filtering. NIPS 2017.[pdf]

57. Balanced neighborhoods for fairness-aware collaborative recommendation. RecSys 2017.[pdf]

58. Controlling popularity bias in learning-to-rank recommendation. RecSys 2017.[pdf]

59. Considerations on recommendation independence for a find-good-items task. Recsys 2017.[pdf]

60. New fairness metrics for recommendation that embrace differences. FAT/ML 2017.[pdf]

61. Fairness-aware group recommendation with pareto-efficiency. RecSys 2017.[pdf]

62. Counterfactual fairness. arXiv 2017.[pdf] [code]

63. Censoring representations with an adversary. ICLR 2016.[pdf]

64. Model-based approaches for independence-enhanced recommendation. IEEE 2016.[pdf] [code]

65. Automated experiments on ad privacy settings: A tale of opacity, choice, and discrimination. Arxiv 2015.[pdf] [code]

66. Efficiency improvement of neutrality-enhanced recommendation.. RecSys 2013.[pdf] [code]

67. Learning fair representations. JMLR 2013.[pdf]

68. Enhancement of the neutrality in recommendation. RecSys 2012.[pdf]

69. Discrimination-aware data mining. KDD 2008.[pdf]

70. Bias in computer systems. TOIS 1996.[pdf]

3.9 Loop Effect

1. Toward Pareto Efficient Fairness-Utility Trade-off inRecommendation through Reinforcement Learning. WSDM 2022.[pdf]

2. AutoDebias: Learning to Debias for Recommendation. SIGIR 2021.[pdf] [code]

3. A general knowledge distillation framework for counterfactual recommendation via uniform data. SIGIR 2020.[pdf] [code]

4. Influence function for unbiased recommendation. SIGIR 2020.[pdf]

5. Understanding echo chambers in e-commerce recommender systems. SIGIR 2020.[pdf] [code]

6. Jointly learning to recommend and advertise. KDD 2020.[pdf]

7. Counterfactual evaluation of slate recommendations with sequential reward interactions. KDD 2020.[pdf] [code]

8. Joint policy value learning for recommendation. KDD 2020.[pdf] [code]

9. Feedback loop and bias amplification in recommender systems. CIKM 2020.[pdf]

10. Degenerate feedback loops in recommender systems. AIES 2019.[pdf]

11. When people change their mind: Off-policy evaluation in non-stationary recommendation environments. WSDM 2019.[pdf] [code]

12. Top-k off-policy correction for a reinforce recommender system. WSDM 2019.[pdf] [code]

13. Improving ad click prediction by considering non-displayed events. CIKM 2019.[pdf] [code]

14. Large-scale interactive recommendation with tree-structured policy gradient. AAAI 2019.[pdf] [code]

15. Deep reinforcement learning for list-wise recommendations. KDD 2019.[pdf] [code]

16. Causal embeddings for recommendation. RecSys 2018.[pdf] [code]

17. How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. RecSys 2018.[pdf]

18. Stabilizing reinforcement learning in dynamic environment with application to online recommendation. KDD 2018.[pdf]

19. Recommendations with negative feedback via pairwise deep reinforcement learning. KDD 2018.[pdf]

20. Drn: A deep reinforcement learning framework for news recommendation. WWW 2018.[pdf]

21. Deep reinforcement learning for page-wise recommendations. RecSys 2018.[pdf]

22. A reinforcement learning framework for explainable recommendation. ICDM 2018.[pdf]

23. Interactive social recommendation. CIKM 2017.[pdf]

24. Off-policy evaluation for slate recommendation. NIPS 2017.[pdf] [code]

25. Factorization bandits for interactive recommendation. WWW 2016.[pdf]

26. Deconvolving feedbackloops in recommender systems. NIPS 2016.[pdf]

27. Interactive collaborative filtering. CIKM 2013.[pdf] [code]

28. A contextual-bandit approach to personalized news article recommendation. WWW 2010.[pdf] [code]

3.10 Other Bias

1. Counteracting User Attention Bias in Music Streaming Recommendation via Reward Modification. KDD 2022.[pdf]

2. Deconfounding Duration Bias inWatch-time Prediction for Video Recommendation. KDD 2022.[pdf] [code]

3. Causal Intervention for Sentiment De-biasing in Recommendation. CIKM 2022.[pdf]

4. Mitigating Sentiment Bias for Recommender Systems. SIGIR 2021.[pdf]

5. Debiasing Learning based Cross-domain Recommendation. KDD 2021.[pdf]

Tips

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