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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