├── Introduction of bias.md ├── README.md └── Survey and Tutorial ├── Survey_Bias and Debias in Recommender System- A Survey and Future Directions.pdf ├── Tutorial_Bias Issues and Solutions in Recommender System.pdf └── Tutorial_Slides_Bias Issues and Solutions in Recommender System.pdf /Introduction of bias.md: -------------------------------------------------------------------------------- 1 | ## Introduction and Analyses of Bias 2 | 3 | | Papers | Bias | Methods | Conference | Code | 4 | | ------------------------------------------------------------ | ------------------- | -------------------- | ----------------------- | ------------------------------------------------------------ | 5 | | [Probabilistic matrix factorization with non-random missing data](http://proceedings.mlr.press/v32/hernandez-lobatob14.pdf) | **Selection Bias** | MF-MNAR | PMLR 2014 | [Python](http://jmhl.org.) | 6 | | [Evaluation of recommendations: rating-prediction and ranking](https://dl.acm.org/doi/pdf/10.1145/2507157.2507160) | Selection Bias | | RecSys 2013 | | 7 | | [Collaborative filtering and the missing at random assumption](https://arxiv.org/ftp/arxiv/papers/1206/1206.5267.pdf) | Selection Bias | | UAI 2007 | [Python](https://github.com/rfenzo/ProyectoRecomendadores) | 8 | | | | | | | 9 | | [Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation](https://arxiv.org/pdf/2109.07946.pdf) | **Conformity Bias** | TIDE | Arxiv 2021 | To publish | 10 | | [When Sheep Shop: Measuring Herding Effects in Product Ratings with Natural Experiments](https://arxiv.org/abs/1802.06578) | Conformity Bias | | WWW 2018 | [Python](https://github.com/epfl-dlab/when_sheep_shop) | 11 | | [Learning personalized preference of strong and weak ties for social Recommendation](https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?referer=https://scholar.google.com.hk/&httpsredir=1&article=4657&context=sis_research) | Conformity Bias | PTPMF | WWW 2017 | | 12 | | [Are you influenced by others when rating?: Improve rating prediction by conformity modeling](https://dl.acm.org/doi/pdf/10.1145/2959100.2959141) | Conformity Bias | | RecSys 2016 | | 13 | | [A probabilistic model for using social networks in personalized item Recommendation](http://www.cs.columbia.edu/~blei/papers/ChaneyBleiEliassi-Rad2015.pdf) | Conformity Bias | SPF | RecSys 2015 | [Python](https://github.com/ajbc/spf) | 14 | | [Why amazon’s ratings might mislead you: The story of herding effects](https://www.liebertpub.com/doi/10.1089/big.2014.0063) | Conformity Bias | | Big data 2014 | | 15 | | [A methodology for learning, analyzing, and mitigating social influence bias in recommender systems](https://amplab.cs.berkeley.edu/wp-content/uploads/2014/07/krishnan-recsys-v16.pdf) | Conformity Bias | BIC | RecSys 2014 | [Python](http://californiareportcard.org/data/) | 16 | | [mtrust: discerning multi-faceted trust in a connected world](http://www.public.asu.edu/~huanliu/papers/wsdm12.pdf) | Conformity Bias | mTrust | WSDM 2012 | | 17 | | [Learning to recommend with social trust ensemble](https://www.researchgate.net/profile/Michael-Lyu/publication/221299915_Learning_to_Recommend_with_Social_Trust_Ensemble/links/5461d7dd0cf27487b4530caa/Learning-to-Recommend-with-Social-Trust-Ensemble.pdf) | Conformity Bias | RSTE | SIGIR 2009 | | 18 | | | | | | | 19 | | [Fast adaptively weighted matrix factorization for recommendation with implicit feedback](https://ojs.aaai.org/index.php/AAAI/article/view/5751) | **Exposure Bias** | FAWMF | AAAI 2020 | | 20 | | [Correcting for selection bias in learning-to-rank systems](https://arxiv.org/pdf/2001.11358.pdf) | Exposure Bias | Heckman | WWW 2020 | | 21 | | [A general knowledge distillation framework for counterfactual recommendation via uniform data](http://csse.szu.edu.cn/staff/panwk/publications/Conference-SIGIR-20-KDCRec.pdf) | Exposure Bias | KDCRec | SIGIR 2020 | [Python](https://github.com/dgliu/SIGIR20_KDCRec) | 22 | | [Samwalker: Social recommendation with informative sampling strategy](https://jiawei-chen.github.io/paper/SamWalker.pdf) | Exposure Bias | Samwalker | WWW 2019 | [MATLAB/C++](https://github.com/jiawei-chen/Samwalker) | 23 | | [Learning to rank with selection bias in personal search](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/45286.pdf) | Exposure Bias | | SIGIR 2016 | | 24 | | | | | | | 25 | | [A study of position bias in digital library recommender systems](https://arxiv.org/ftp/arxiv/papers/1802/1802.06565.pdf) | **Position Bias** | | arXiv 2018 | | 26 | | [Accurately interpreting clickthrough data as implicit feedback](http://www.sigir.org/wp-content/uploads/2017/06/p004.pdf) | Position Bias | | SIGIR 2016 | | 27 | | [Evaluating the accuracy of implicit feedback from clicks and query reformulations in web search](http://sing.stanford.edu/cs303-sp10/papers/joachims_etal_07a.pdf) | Position Bias | | ACM Journals 2007 | | 28 | | [Modeling result-list searching in the world wide web: The role of relevance topologies and trust bias](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.98.516&rep=rep1&type=pdf) | Position Bias | | CogSci 2006 | | 29 | | | | | | | 30 | | [Discrete content-aware matrix factorization](https://zheng-kai.com/paper/kdd_2017_lian.pdf) | **Inductive Bias** | DCMF | KDD 2017 | | 31 | | [Neural collaborative filtering](https://arxiv.org/pdf/1708.05031.pdf?source=post_page---------------------------) | Inductive Bias | NCF | WWW 2017 | [Python](https://github.com/hexiangnan/neural_collaborative_filtering) | 32 | | [Discrete collaborative filtering](http://staff.ustc.edu.cn/~hexn/papers/sigir16-dcf-cm.pdf) | Inductive Bias | DCF | SIGIR 2016 | | 33 | | [Logistic matrix factorization for implicit feedback data](http://web.stanford.edu/~rezab/nips2014workshop/submits/logmat.pdf) | Inductive Bias | LogisticMF | NIPS 2014 | | 34 | | [Learning binary codes for collaborative filtering](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.906.4707&rep=rep1&type=pdf) | Inductive Bias | CFCodeReg/CFCodePair | KDD 2012 | | 35 | | | | | | | 36 | | [Popularity Bias in Dynamic Recommendation](https://dl.acm.org/doi/pdf/10.1145/3447548.3467376) | **Popularity Bias** | FPC | KDD 2021 | [Tensorflow](https://github.com/Zziwei/Popularity-Bias-in-Dynamic-Recommendation) | 37 | | [Popularity-Opportunity Bias in Collaborative Filtering](https://dl.acm.org/doi/pdf/10.1145/3437963.3441820) | Popularity Bias | | WSDM 2021 | | 38 | | [Multi-sided exposure bias in recommendation](https://arxiv.org/pdf/2006.15772.pdf) | Popularity Bias | | Arxiv 2020 | | 39 | | [The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation](https://dl.acm.org/doi/pdf/10.1145/3383313.3418487) | Popularity Bias | | Recsys 2020 | | 40 | | [A survey on bias and fairness in machine learning](https://arxiv.org/pdf/1908.09635.pdf) | Popularity Bias | | Arxiv 2019 | | 41 | | [A Probabilistic Reformulation of Memory-Based Collaborative Filtering – Implications on Popularity Biases](https://dl.acm.org/doi/pdf/10.1145/3077136.3080836) | Popularity Bias | | SIGIR 2017 | | 42 | | [What recommenders recommend: an analysis of recommendation biases and possible countermeasures](https://link.springer.com/article/10.1007/s11257-015-9165-3) | Popularity Bias | | UMUAI 2015 | | 43 | | | | | | | 44 | | [User Bias in Beyond-Accuracy Measurement of Recommendation Algorithms](http://www.comp.hkbu.edu.hk/~lichen/download/Ningxia_RecSys21.pdf) | **Unfairness** | | Recys 2021 | | 45 | | [Exploring author gender in book rating and recommendation](https://arxiv.org/pdf/1808.07586.pdf) | Unfairness | | 2020 | [Python](https://github.com/BoiseState/bookdata-tools) | 46 | | [Algorithmic bias? an empirical study of apparent gender-based discrimination in the display of stem career ads](https://lbsresearch.london.edu/id/eprint/967/1/AlgorithmicBias_Mar2018.pdf) | Unfairness | | INFORMS 2019 | | 47 | | [Crank up the volume: preference bias amplification in collaborative recommendation](https://arxiv.org/pdf/1909.06362.pdf) | Unfairness | | Recys 2019 | | 48 | | [Homophily influences ranking of minorities in social networks](https://www.nature.com/articles/s41598-018-29405-7.pdf) | Unfairness | | Scientific Reports 2018 | | 49 | | [Algorithmic glass ceiling in social networks: The effects of social recommendations on network diversity](http://www.columbia.edu/~as5001/algglassceiling.pdf) | Unfairness | | WWW 2018 | | 50 | | [Automated experiments on ad privacy settings: A tale of opacity, choice, and discrimination](https://arxiv.org/pdf/1408.6491.pdf) | Unfairness | AdFisher | Arxiv 2015 | [Python](http://www.cs.cmu.edu/~mtschant/ife/) | 51 | | [Bias in computer systems](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.17.6776&rep=rep1&type=pdf) | Unfairness | | TOIS 1996 | | 52 | | | | | | | 53 | | [Feedback loop and bias amplification in recommender systems](https://arxiv.org/pdf/2007.13019.pdf) | **Loop Effect** | | CIKM 2020 | | 54 | | [Understanding echo chambers in e-commerce recommender systems ](https://arxiv.org/pdf/2007.02474.pdf) | Loop Effect | | SIGIR 2020 | [Python](https://github.com/szhaofelicia/EchoChamberInEcommerce) | 55 | | [Degenerate feedback loops in recommender systems](https://arxiv.org/pdf/1902.10730.pdf) | Loop Effect | *Oracle* | AIES 2019 | | 56 | | [How algorithmic confounding in recommendation systems increases homogeneity and decreases utility](https://arxiv.org/pdf/1710.11214.pdf) | Loop Effect | | RecSys 2018 | | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Recommendation Debiasing 2 | 3 | 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. 4 | 5 | 6 | ### Contents 7 | * [1. Survey Papers](#1-survey-papers) 8 | * [2. Datasets](#2-datasets) 9 | * [3. Debiasing Strategies](#3-debiasing-strategies) 10 | * [3.1 Multiply Biases](#31-multiply-biases) 11 | * [3.2 Selection Bias](#32-selection-bias) 12 | * [3.3 Conformity Bias](#33-conformity-bias) 13 | * [3.4 Exposure Bias](#34-exposure-bias) 14 | * [3.5 Position Bias](#35-position-bias) 15 | * [3.6 Popularity Bias](#36-popularity-bias) 16 | * [3.7 Inductive Bias](#37-inductive-bias) 17 | * [3.8 Unfairness](#38-unfairness) 18 | * [3.9 Loop Effect](#39-loop-effect) 19 | * [3.10 Other Bias](#310-other-bias) 20 | 21 | * [Tips](#tips) 22 | 23 | 24 | ## 1. Survey Papers 25 | 1. **A Survey on the Fairness of Recommender Systems**. TOIS 2023. [[pdf](https://dl.acm.org/doi/pdf/10.1145/3547333)] 26 | 1. **Bias and Debias in Recommender System: A Survey and Future Directions**. TOIS 2023. [[pdf](https://arxiv.org/pdf/2010.03240.pdf)] 27 | 2. **Bias Issues and Solutions in Recommender System**. WWW 2021,Recsys 2021. [[pdf](http://staff.ustc.edu.cn/~hexn/papers/recsys21-tutorial-bias.pdf)] 28 | 29 | 2. **A survey on bias and fairness in machine learning**. Arxiv 2019. [[pdf](https://arxiv.org/pdf/1908.09635.pdf)] 30 | 31 | 32 | ## 2. Datasets 33 | We collect some datasets which include unbiased data and are often used in the research of recommendation debiasing. 34 | 1. **Yahoo!R3: Collaborative Prediction and Ranking with Non-Random Missing Data**. Recsys 2009. [[pdf](https://www.cs.toronto.edu/~zemel/documents/acmrec2009-MarlinZemel.pdf)][[data](https://webscope.sandbox.yahoo.com/catalog.php?datatype=r)] 35 | 2. **Coat: Recommendations as Treatments: Debiasing Learning and Evaluation**. ICML 2016. [[pdf](https://arxiv.org/abs/1602.05352)][[data](https://www.cs.cornell.edu/~schnabts/mnar/)] 36 | 3. **KuaiRec: A Fully-observed Dataset for Recommender Systems**. CIKM 2022. [[pdf](https://arxiv.org/abs/2202.10842)][[data](https://chongminggao.github.io/KuaiRec/)] 37 | 38 | 4. **KuaiRand: An Unbiased Sequential Recommendation Dataset 39 | with Randomly Exposed Videos**. CIKM 2022.[[pdf](https://arxiv.org/pdf/2208.08696.pdf)][[data](https://kuairand.com/)] 40 | 41 | 42 | ## 3. Debiasing Strategies 43 | 44 | ### 3.1 Multiply Biases 45 | 46 | 1. **Bounding System-Induced Biases in Recommender Systems with a Randomized Dataset**. TOIS 2023.[[pdf](https://dl.acm.org/doi/10.1145/3582002)] [[code](https://github.com/dgliu/TOIS_DUB)] 47 | 48 | 1. **Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations**. WWW 2023.[[pdf](https://dl.acm.org/doi/10.1145/3543507.3583495)] 49 | 50 | 1. **Transfer Learning in Collaborative Recommendation for Bias Reduction**. Recsys 2021.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3460231.3478860)] [[code](http://csse.szu.edu.cn/staff/panwk/publications/TJR/)] 51 | 1. **AutoDebias: Learning to Debias for Recommendation**. SIGIR 2021.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3404835.3462919)] [[code](https://github.com/DongHande/AutoDebias)] 52 | 1. **A General Knowledge Distillation Framework for Counterfactual Recommendation via Uniform Data**. SIGIR 2020.[[pdf](https://dgliu.github.io/files/SIGIR20_KDCRec.pdf)] [[code](https://github.com/dgliu/SIGIR20_KDCRec)] 53 | 1. **Causal Embeddings for Recommendation**. Recsys 2018.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3240323.3240360)] [[code](https://github.com/criteo-research/CausE)] 54 | 55 | 56 | 57 | ### 3.2 Selection Bias 58 | 1. **Reconsidering Learning Objectives in Unbiased Recommendation A Distribution Shift Perspective**. KDD 2023.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3580305.3599487)] 59 | 1. **Propensity Matters Measuring and Enhancing Balancing for Recommendation**. ICML 2023.[[pdf](https://dl.acm.org/doi/10.5555/3618408.3619239)] 60 | 1. **A Generalized Propensity Learning Framework for Unbiased Post-Click Conversion Rate Estimation**. CIKM 2023.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3583780.3614760)] [[code](https://github.com/yuqing-zhou/GPL)] 61 | 1. **CDR: Conservative Doubly Robust Learning for Debiased Recommendation**. CIKM 2023.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3583780.3614805)] [[code](https://github.com/crazydumpling/CDR_CIKM2023)] 62 | 1. **UKD: Debiasing Conversion Rate Estimation via Uncertainty-regularized Knowledge Distillation**. WWW 2022.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3485447.3512081)] 63 | 1. **Practical Counterfactual Policy Learning for Top-𝐾 Recommendations**. KDD 2022.[[pdf](https://dl.acm.org/doi/abs/10.1145/3534678.3539295)] 64 | 1. **Debiasing Neighbor Aggregation for Graph Neural Network in Recommender Systems**. CIKM 2022.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3511808.3557576)] 65 | 1. **Representation Matters When Learning From Biased Feedback in Recommendation**. CIKM 2022.[[pdf](https://dl.acm.org/doi/10.1145/3511808.3557431)] 66 | 1. **Hard Negatives or False Negatives: Correcting Pooling Bias in Training Neural Ranking Models**. CIKM 2022.[[pdf](https://dl.acm.org/doi/abs/10.1145/3511808.3557343)] 67 | 1. **Be Causal: De-biasing Social Network Confounding in Recommendation**. TKDD 2022.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3533725)] 68 | 69 | 1. **Debiased recommendation with neural stratification**. AI OPEN 2022.[[pdf](https://arxiv.org/abs/2208.07281)] 70 | 71 | 72 | 1. **ESCM2: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation**. SIGIR 2022.[[pdf](https://arxiv.org/abs/2204.05125)] 73 | 1. **Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction**. KDD 2022.[[pdf](https://dl.acm.org/doi/10.1145/3534678.3539270)] 74 | 1. **Combating Selection Biases in Recommender Systems with a Few Unbiased Ratings**. WSDM 2021.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3437963.3441799)] 75 | 1. **Doubly Robust Estimator for Ranking Metrics with Post‐Click Conversions**. RecSys 2020.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3383313.3412262)] [[code](https://github.com/usaito/dr-ranking-metric)] 76 | 1. **Asymmetric tri-training for debiasing missing-not-at-random explicit feedback**. SIGIR 2020.[[pdf]((https://arxiv.org/pdf/1910.01444.pdf))] 77 | 1. **Recommendations as treatments: Debiasing learning and evaluation**. ICML 2016.[[pdf](http://proceedings.mlr.press/v48/schnabel16.pdf)] [[code](https://www.cs.cornell.edu/~schnabts/mnar/)] 78 | 1. **Doubly robust joint learning for recommendation on data missing not at random**. ICML 2019.[[pdf](http://proceedings.mlr.press/v97/wang19n/wang19n.pdf)] 79 | 1. **The deconfounded recommender: A causal inference approach to recommendation**. arXiv 2018.[[pdf](https://arxiv.org/pdf/1808.06581.pdf)] 80 | 1. **Social recommendation with missing not at random data**. ICDM 2018.[[pdf](https://ieeexplore.ieee.org/abstract/document/8594827)] 81 | 82 | 1. **Recommendations as treatments: Debiasing learning and evaluation**. [[pdf](http://proceedings.mlr.press/v48/schnabel16.pdf)] 83 | 1. **Boosting Response Aware Model-Based Collaborative Filtering**. TKDE 2015.[[pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7045598)] 84 | 1. **Probabilistic matrix factorization with non-random missing data**. PMLR 2014.[[pdf](http://proceedings.mlr.press/v32/hernandez-lobatob14.pdf)] [[code](https://jmhl.org/)] 85 | 1. **Bayesian Binomial Mixture Model for Collaborative Prediction With Non-Random Missing Data**. RecSys 2014.[[pdf](https://dl.acm.org/doi/pdf/10.1145/2645710.2645754)] 86 | 1. **Evaluation of recommendations: rating-prediction and ranking**. RecSys 2013.[[pdf](https://dl.acm.org/doi/pdf/10.1145/2507157.2507160)] 87 | 1. **Training and testing of recommender systems on data missing not at random**. KDD 2010.[[pdf](https://dl.acm.org/doi/abs/10.1145/1835804.1835895)] 88 | 1. **Collaborative prediction and ranking with non-random missing data**. RecSys 2009.[[pdf](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.154.7692&rep=rep1&type=pdf)] 89 | 1. **Collaborative filtering and the missing at random assumption**. UAI 2007.[[pdf](https://arxiv.org/ftp/arxiv/papers/1206/1206.5267.pdf)] [[code](https://github.com/rfenzo/ProyectoRecomendadores)] 90 | 91 | 92 | ### 3.3 Conformity Bias 93 | 94 | 1. **Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation**. TKDE 2022.[[pdf](https://arxiv.org/pdf/2109.07946.pdf)] 95 | 1. **Disentangling user interest and Conformity for recommendation with causal embedding**. WWW 2021.[[pdf](https://arxiv.org/pdf/2006.11011.pdf)] [[code](https://github.com/tsinghua-fib-lab/DICE)] 96 | 1. **When Sheep Shop: Measuring Herding Effects in Product Ratings with Natural Experiments**. WWW 2018.[[pdf](https://arxiv.org/abs/1802.06578)] [[code](https://github.com/epfl-dlab/when_sheep_shop)] 97 | 1. **Learning personalized preference of strong and weak ties for social recommendation**. WWW 2017.[[pdf](https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?referer=https://scholar.google.com.hk/&httpsredir=1&article=4657&context=sis_research)] 98 | 1. **Are you influenced by others when rating?: Improve rating prediction by conformity modeling**. RecSys 2016.[[pdf](https://dl.acm.org/doi/10.1145/2959100.2959141)] 99 | 1. **Xgboost: A scalable tree boosting system**. KDD 2016.[[pdf](https://arxiv.org/pdf/1603.02754.pdf?__hstc=133736337.1bb630f9cde2cb5f07430159d50a3c91.1513641600097.1513641600098.1513641600099.1&__hssc=133736337.1.1513641600100&__hsfp=528229161)] [[code](https://github.com/dmlc/xgboost)] 100 | 101 | 1. **A probabilistic model for using social networks in personalized item recommendation**. RecSys 2015.[[pdf](http://www.cs.columbia.edu/~blei/papers/ChaneyBleiEliassi-Rad2015.pdf)] [[code](https://github.com/ajbc/spf)] 102 | 1. **Why amazon’s ratings might mislead you: The story of herding effects**. Big data 2014.[[pdf](https://amplab.cs.berkeley.edu/wp-content/uploads/2014/07/krishnan-recsys-v16.pdf)] 103 | 1. **A methodology for learning, analyzing, and mitigating social influence bias in recommender systems**. RecSys 2014.[[pdf](https://amplab.cs.berkeley.edu/wp-content/uploads/2014/07/krishnan-recsys-v16.pdf)] [[code](http://californiareportcard.org/data/)] 104 | 105 | 1. **Mtrust: discerning multi-faceted trust in a connected world**. WSDM 2012.[[pdf](http://www.public.asu.edu/~huanliu/papers/wsdm12.pdf)] 106 | 1. **Learning to recommend with social trust ensemble**. SIGIR 2009.[[pdf](https://www.researchgate.net/profile/Michael-Lyu/publication/221299915_Learning_to_Recommend_with_Social_Trust_Ensemble/links/5461d7dd0cf27487b4530caa/Learning-to-Recommend-with-Social-Trust-Ensemble.pdf)] 107 | 108 | 109 | 110 | ### 3.4 Exposure Bias 111 | 1. **uCTRL Unbiased Contrastive Representation Learning via Alignment and Uniformity for Collaborative Filtering**. SIGIR 2023.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3539618.3592076)] [[code](https://github.com/Jaewoong-Lee/sigir_2023_uCTRL)] 112 | 1. **Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss**. NIPS 2023.[[pdf](https://papers.nips.cc/paper_files/paper/2023/file/13f1750b825659394a6499399e7637fc-Paper-Conference.pdf)] [[code](https://github.com/LehengTHU/AdvInfoNCE)] 113 | 1. **Debiasing Sequential Recommenders through Distributionally Robust Optimization over System Exposure**. WSDM 2023.[[pdf](https://arxiv.org/abs/2312.07036)] [[code](https://github.com/nancheng58/DebiasedSR_DRO)] 114 | 1. **Debiasing the Cloze Task in Sequential Recommendation with Bidirectional Transformers**. KDD 2022.[[pdf](https://dl.acm.org/doi/abs/10.1145/3534678.3539430)] 115 | 1. **Debiasing Neighbor Aggregation for Graph Neural Network in Recommender Systems**. CIKM 2022.[[pdf](https://arxiv.org/abs/2208.08847)] 116 | 1. **Non-Clicks Mean Irrelevant? Propensity Ratio Scoring As a Correction**. WSDM 2021.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3437963.3441798)] 117 | 1. **Propensity-Independent Bias Recovery in Offline Learning-to-Rank Systems**. SIGIR 2021.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3404835.3463097)] 118 | 1. **Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue**. SIGIR 2021.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3404835.3462962)] [[code](https://github.com/WenjieWWJ/Clickbait/)] 119 | 1. **Mitigating Confounding Bias in Recommendation via Information Bottleneck**. Recsys 2021.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3460231.3474263)] [[code](https://github.com/dgliu/RecSys21_DIB)] 120 | 121 | 1. **Debiased Explainable Pairwise Ranking from Implicit Feedback**. Recsys 2021.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3460231.3474274)] [[code](https://github.com/KhalilDMK/EBPR)] 122 | 1. **Top-N Recommendation with Counterfactual User Preference Simulation**. CIKM 2021.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3459637.3482305)] 123 | 1. **SamWalker++: recommendation with informative sampling strategy**. TKDE 2021.[[pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9507306)] [[code](https://github.com/jiawei-chen/SamWalker)] 124 | 1. **Deconfounded Causal Collaborative Filtering**. Arxiv 2021/TORS 2023.[[pdf](https://dlnext.acm.org/doi/pdf/10.1145/3606035)] 125 | 1. **Unbiased recommender learning from missing-not-at-random implicit feedback**. WSDM 2020.[[pdf](https://arxiv.org/pdf/1909.03601.pdf)] [[code](https://github.com/usaito/unbiased-implicit-rec)] 126 | 1. **Reinforced negative sampling over knowledge graph for recommendation**. WWW 2020.[[pdf](https://arxiv.org/pdf/2003.05753.pdf)] [[code](https://github.com/xiangwang1223/kgpolicy)] 127 | 1. **Fast adaptively weighted matrix factorization for recommendation with implicit feedback**. AAAI 2020.[[pdf](https://ojs.aaai.org/index.php/AAAI/article/view/5751)] 128 | 1. **Correcting for selection bias in learning-to-rank systems**. WWW 2020.[[pdf](https://arxiv.org/pdf/2001.11358.pdf)] [[code](https://github.com/edgeslab/heckman_rank)] 129 | 1. **Large-scale causal approaches to debiasing post-click conversion rate estimation with multi-task learning**. WWW 2020.[[pdf](https://arxiv.org/pdf/1910.09337.pdf)] 130 | 1. **Entire space multi-task modeling via post-click behavior decomposition for conversion rate prediction**. SIGIR 2020.[[pdf](https://arxiv.org/pdf/1910.07099.pdf)] 131 | 1. **A general knowledge distillation framework for counterfactual recommendation via uniform data**. SIGIR 2020.[[pdf](http://csse.szu.edu.cn/staff/panwk/publications/Conference-SIGIR-20-KDCRec.pdf)] [[code](https://github.com/dgliu/SIGIR20_KDCRec)] 132 | 1. **Unbiased Implicit Recommendation and Propensity Estimation via Combinational Joint Learning**. Recsys 2020.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3383313.3412210)] [[code](https://github.com/Zziwei/Unbiased-Propensity-and-Recommendation)] 133 | 1. **Debiasing Item-to-Item Recommendations With Small Annotated Datasets**. Recsys 2020.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3383313.3412265)] 134 | 1. **Reinforced negative sampling for recommendation with exposure data**. IJCAI 2019.[[pdf](https://www.ijcai.org/Proceedings/2019/0309.pdf)] [[code](https://github.com/dingjingtao/ReinforceNS)] 135 | 136 | 1. **Samwalker: Social recommendation with informative sampling strategy**. WWW 2019.[[pdf](https://jiawei-chen.github.io/paper/SamWalker.pdf)] [[code](https://github.com/jiawei-chen/Samwalker)] 137 | 1. **Collaborative filtering with social exposure: A modular approach to social recommendation**. AAAI 2018.[[pdf](https://ojs.aaai.org/index.php/AAAI/article/view/11835)] [[code](https://github.com/99731/SERec)] 138 | 1. **An improved sampler for bayesian personalized ranking by leveraging view data**. WWW 2018.[[pdf](http://staff.ustc.edu.cn/~hexn/papers/www18-improvedBPR.pdf)] 139 | 1. **Unbiased offline recommender evaluation for missing-not-at-random implicit feedback**. RecSys 2018.[[pdf](https://vision.cornell.edu/se3/wp-content/uploads/2018/08/recsys18_unbiased_eval.pdf)] [[code](https://github.com/ylongqi/unbiased-offline-recommender-evaluation)] 140 | 1. **Entire space multi-task model: An effective approach for estimating post-click conversion rate**. SIGIR 2018.[[pdf](https://arxiv.org/pdf/1804.07931.pdf)] 141 | 1. **Modeling users’ exposure with social knowledge influence and consumption influence for recommendation**. CIKM 2018.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3269206.3271742)] 142 | 1. **Selection of negative samples for one-class matrix factorization**. SDM 2017.[[pdf](https://www.csie.ntu.edu.tw/~cjlin/papers/one-class-mf/biased-mf-sdm-with-supp.pdf)] [[code](https://www.csie.ntu.edu.tw/~cjlin/papers/one-class-mf/)] 143 | 1. **Learning to rank with selection bias in personal search**. SIGIR 2016.[[pdf](https://research.google/pubs/pub45286.pdf)] 144 | 1. **Modeling user exposure in recommendation**. WWW 2016.[[pdf](https://arxiv.org/pdf/1510.07025.pdf)] [[code](https://github.com/dawenl/expo-mf)] 145 | 1. **Collaborative denoising auto-encoders for top-n recommender systems (CDAE)**. WSDM 2016.[[pdf](https://www.datascienceassn.org/sites/default/files/Collaborative%20Denoising%20Auto-Encoders%20for%20Top-N%20Recommender%20Systems.pdf)] [[code](https://github.com/jasonyaw/CDAE)] 146 | 1. **Fast matrix factorization for online recommendation with implicit feedback**. SIGIR 2016.[[pdf](https://arxiv.org/pdf/1708.05024.pdf)] [[code](https://github.com/hexiangnan/sigir16-eals)] 147 | 1. **Dynamic matrix factorization with priors on unknown values**. KDD 2015.[[pdf](https://arxiv.org/pdf/1507.06452.pdf)] [[code](https://github.com/rdevooght/MF-with-prior-and-updates)] 148 | 1. **Logistic matrix factorization for implicit feedback data**. NIPS 2014.[[pdf](http://web.stanford.edu/~rezab/nips2014workshop/submits/logmat.pdf)] 149 | 1. **Improving one-class collaborative filtering by incorporating rich user information**. CIKM 2010.[[pdf](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.228.7135&rep=rep1&type=pdf)] 150 | 1. **Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering**. KDD 2009.[[pdf](https://dl.acm.org/doi/pdf/10.1145/1557019.1557094)] 151 | 1. **Collaborative filtering for implicit feedback datasets**. ICDM 2008.[[pdf](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.167.5120&rep=rep1&type=pdf)] [[code](https://github.com/benfred/implicit)] 152 | 1. **One-class collaborative filtering**. ICDM 2008.[[pdf](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.306.4684&rep=rep1&type=pdf)] 153 | 154 | 155 | ### 3.5 Position Bias 156 | 1. **An Offline Metric for the Debiasedness of Click Models**. SIGIR 2023.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3539618.3591639)] [[code](https://github.com/philipphager/sigir-cmip)] 157 | 1. **A Probabilistic Position Bias Model for Short-Video Recommendation Feeds**. RecSys 2023.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3604915.3608777)] [[code](https://github.com/olivierjeunen/C-3PO-recsys-2023)] 158 | 159 | 1. **Unbiased Learning to Rank with Biased Continuous Feedback**. CIKM 2022.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3511808.3557483)] [[code](https://github.com/phyllist/ULTRA)] 160 | 1. **Scalar is Not Enough: Vectorization-based Unbiased Learning to Rank**. KDD 2022.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3534678.3539468)] [[code](https://github.com/Keytoyze/Vectorization)] 161 | 1. **Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model**. WSDM 2022.[[pdf](https://dl.acm.org/doi/abs/10.1145/3488560.3498380)] [[code](https://github.com/aiueola/wsdm2022-cascade-dr)] 162 | 1. **Can Clicks Be Both Labels and Features?: Unbiased Behavior Feature Collection and Uncertainty-aware Learning to Rank**. SIGIR 2022.[[pdf](https://dl.acm.org/doi/abs/10.1145/3477495.3531948)] [[code](https://github.com/Taosheng-ty/UCBRankSIGIR2022)] 163 | 1. **A Graph-Enhanced Click Model for Web Search**. SIGIR 2021.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3404835.3462895)] [[code](https://github.com/CHIANGEL/GraphCM)] 164 | 1. **Adapting Interactional Observation Embedding for Counterfactual Learning to Rank**. SIGIR 2021.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3404835.3462901)] [[code](https://github.com/Keytoyze/Interactional-Observation-Based-Model)] 165 | 1. **When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to Rank**. CIKM 2020.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3340531.3412031)] [[code](https://github.com/AliVard/trust-bias-CIKM2020/tree/master/trust_bias)] 166 | 1. **A deep recurrent survival model for unbiased ranking**. SIGIR 2020.[[pdf](https://arxiv.org/pdf/2004.14714.pdf)] [[code](https://github.com/Jinjiarui/DRSR)] 167 | 1. **Attribute-based propensity for unbiased learning in recommender systems: Algorithm and case studies**. KDD 2020.[[pdf](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/54a3b73ea1e85e94e5d5bb5a9df821a1f32aa783.pdf)] 168 | 1. **Debiasing grid-based product search in e-commerce**. KDD 2020.[[pdf](https://www.hongliangjie.com/publications/kdd2020_2.pdf)] 169 | 1. **Cascade model-based propensity estimation for counterfactual learning to rank**. SIGIR 2020.[[pdf](https://arxiv.org/pdf/2005.11938.pdf))] [[code](https://github.com/AliVard/CM-IPS-SIGIR20)] 170 | 1. **Addressing Trust Bias for Unbiased Learning-to-Rank**. WWW 2019.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3308558.3313697)] 171 | 1. **Position bias estimation for unbiased learning to rank in personal search**. WSDM 2018.[[pdf](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/3bace79f9bcead0b20dec31e2a0878346ad2fb0d.pdf)] 172 | 173 | 174 | 1. **A study of position bias in digital library recommender systems**. ArXiv 2018.[[pdf](https://arxiv.org/ftp/arxiv/papers/1802/1802.06565.pdf)] 175 | 176 | 1. **Offline Evaluation of Ranking Policies with Click Models**. KDD 2018.[[pdf](https://dl.acm.org/doi/abs/10.1145/3219819.3220028)] 177 | 1. **Unbiased learning to rank with unbiased propensity estimation**. SIGIR 2018.[[pdf](https://arxiv.org/pdf/1804.05938.pdf)] [[code](https://github.com/QingyaoAi/Dual-Learning-Algorithm-for-Unbiased-Learning-to-Rank)] 178 | 1. **Unbiased learning-to-rank with biased feedback**. WSDM 2017.[[pdf](https://arxiv.org/pdf/1608.04468.pdf)] [[code](https://www.cs.cornell.edu/people/tj/svm_light/svm_rank.html)] 179 | 1. **Multileave gradient descent for fast online learning to rank**. WSDM 2016.[[pdf](https://ilps.science.uva.nl/wp-content/uploads/sites/8/2015/11/DIR2015-proceedings.pdf#page=14)] 180 | 1. **Learning to rank with selection bias in personal search**. SIGIR 2016.[[pdf](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/45286.pdf)] 181 | 1. **Accurately interpreting clickthrough data as implicit feedback**. SIGIR 2016.[[pdf](http://www.sigir.org/wp-content/uploads/2017/06/p004.pdf)] 182 | 1. **Batch learning from logged bandit feedback through counterfactual risk minimization**. JMLR 2015.[[pdf](https://www.jmlr.org/papers/volume16/swaminathan15a/swaminathan15a.pdf)] 183 | 1. **Learning socially optimal information systems from egoistic users**. ECML PKDD 2013.[[pdf](https://link.springer.com/content/pdf/10.1007/978-3-642-40991-2_9.pdf)] 184 | 1. **Reusing historical interaction data for faster online learning to rank for ir**. WSDM 2013.[[pdf](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.408.196&rep=rep1&type=pdf)] [[code](https://ilps.science.uva.nl/resources/online-learning-framework)] 185 | 1. **A novel click model and its applications to online advertising**. WSDM 2010.[[pdf](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/WSDM2010.pdf)] 186 | 1. **A dynamic bayesian network click model for web search ranking**. WWW 2009.[[pdf](http://www2009.eprints.org/1/1/p1.pdf)] 187 | 1. **Click chain model in web search**. WWW 2009.[[pdf](http://www2009.eprints.org/2/1/p11.pdf)] 188 | 1. **A user browsing model to predict search engine click data from past observations**. SIGIR 2008.[[pdf](https://www.researchgate.net/profile/Georges-Dupret/publication/200110492_A_user_browsing_model_to_predict_search_engine_click_data_from_past_observations/links/54e4c5ea0cf29865c3351048/A-user-browsing-model-to-predict-search-engine-click-data-from-past-observations.pdf)] 189 | 1. **An experimental comparison of click position-bias models**. WSDM 2008.[[pdf](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.112.1288&rep=rep1&type=pdf)] 190 | 1. **Comparing click logs and editorial labels for training query rewriting**. WWW 2007.[[pdf](https://www2007.org/workshops/paper_63.pdf)] 191 | 192 | 1. **Evaluating the accuracy of implicit feedback from clicks and query reformulations in web search**. ACM Journals 2007.[[pdf](http://sing.stanford.edu/cs303-sp10/papers/joachims_etal_07a.pdf)] 193 | 1. **Modeling result-list searching in the world wide web: The role of relevance topologies and trust bias**. CogSci 2006.[[pdf](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.98.516&rep=rep1&type=pdf)] 194 | 195 | 196 | ### 3.6 Popularity Bias 197 | 1. **TCCM Time and Content-Aware Causal Model for Unbiased News Recommendation**. CIKM 2023.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3583780.3615272)] [[code](https://github.com/XFastDataLab/TCCM)] 198 | 1. **Rlieving Popularity Bias in Interactive Recommendation A Diversity-Novelty-Aware Reinforcement Learning Approach**. TOIS 2023.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3618107)] [[code](https://github.com/shixiaoyu0216/DNaIR)] 199 | 1. **Test-Time Embedding Normalization for Popularity Bias Mitigation**. CIKM 2023.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3583780.3615281)] [[code](https://github.com/ml-postech/TTEN)] 200 | 1. **Potential Factors Leading to Popularity Unfairness in Recommender Systems A User-Centered Analysis**. Arxiv 2023.[[pdf](https://arxiv.org/pdf/2310.02961.pdf)] 201 | 1. **Mitigating the Popularity Bias of Graph Collaborative Filtering A Dimensional Collapse Perspective**. NIPS 2023.[[pdf](https://papers.nips.cc/paper_files/paper/2023/file/d5753be6f71fbfefaf47aa27ec41279c-Paper-Conference.pdf)] [[code](https://github.com/yifeiacc/LogDet4Rec/)] 202 | 1. **A Model-Agnostic Popularity Debias Training Framework for Click-Through Rate Prediction in Recommender System**. SIGIR 2023.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3539618.3591939)] 203 | 1. **Popularity Debiasing from Exposure to Interaction in Collaborative Filtering**. SIGIR 2023.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3539618.3591947)] [[code](https://github.com/UnitDan/IPL)] 204 | 1. **Adaptive Popularity Debiasing Aggregator for Graph Collaborative Filtering**. SIGIR 2023.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3539618.3591635)] 205 | 1. **HDNR A Hyperbolic-Based Debiased Approach for Personalized News Recommendation**. SIGIR 2023.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3539618.3591693)] 206 | 1. **MELT Mutual Enhancement of Long-Tailed User and Item for Sequential Recommendation**. SIGIR 2023.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3539618.3591725)] [[code](https://github.com/rlqja1107/MELT)] 207 | 208 | 1. **Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random**. ICLR 2023.[[pdf](https://arxiv.org/abs/2205.04701)] 209 | 1. **Invariant Collaborative Filtering to Popularity Distribution Shift**. WWW 2023.[[pdf](https://dl.acm.org/doi/10.1145/3543507.3583461)] [[code](https://github.com/anzhang314/InvCF)] 210 | 1. **Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering**. SIGIR 2022.[[pdf](https://dl.acm.org/doi/10.1145/3477495.3532005)] [[code](https://github.com/fuxiAIlab/r-AdjNorm)] 211 | 1. **Evolution of Popularity Bias: Empirical Study and Debiasing**. KDD 2022.[[pdf](https://arxiv.org/abs/2207.03372)] [[code](https://github.com/Zziwei/Popularity-Bias-in-Dynamic-Recommendation)] 212 | 1. **Countering Popularity Bias by Regularizing Score Differences**. RecSys 2022.[[pdf](https://dl.acm.org/doi/10.1145/3523227.3546757)] [[code](https://github.com/stillpsy/popbias)] 213 | 1. **Co-training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommenders**. SIGIR 2022.[[pdf](https://dl.acm.org/doi/abs/10.1145/3477495.3531952)] 214 | 1. **Popularity bias in ranking and recommendation**. AIES 2019.[[pdf](https://d1wqtxts1xzle7.cloudfront.net/59543380/AIES2019.pdf?1559792566=&response-content-disposition=inline%3B+filename%3DPopularity_Bias_in_Ranking_and_Recommend.pdf&Expires=1617615017&Signature=X8T9bB3TRTGzFfbDfyMQANbJrYQMreC3fKdt10LzBeM2SY8hpe0ovwZHtMxe2DkJAi0OVrq24e~xu3S3GHG434z7MPhgHH7e28Jy61PSVQsy-IgxM3XzeVoPZzNVaS6R8GCVKpX1Ho4XZjfzsRXaP-50wFtFtOczmLTdDmCfFzpi9ngCIAAmjQEXaTUIRUPxPnTF20JYZLuDymUfQiC7CuZNceg9FsfqFp1ON86aQVfmNiI6VBZIi1Sy0akDTjaTujYplbl4vfAuOTbx5JfhjPDV5fwLB~M~cxFFDYCtqM8PvJO-fVZqObrW3ftZCz2LoRJyy0ve1IGPYb3jAJ7oag__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA)] 215 | 1. **Disentangling User Interest and Conformity for Recommendation with Causal Embedding**. WWW 2021.[[pdf](https://arxiv.org/pdf/2006.11011.pdf)] [[code](https://github.com/tsinghua-fib-lab/DICE)] 216 | 1. **The Unfairness of Popularity Bias in Recommendation**. SAC 2021.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3412841.3442123)] [[code](https://github.com/rcaborges/popularity-bias-vae)] 217 | 1. **Popularity Bias in Dynamic Recommendation**. KDD 2021.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3447548.3467376)] [[code](https://github.com/Zziwei/Popularity-Bias-in-Dynamic-Recommendation)] 218 | 1. **Causal Intervention for Leveraging Popularity Bias in Recommendation**. SIGIR 2021.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3404835.3462875)] [[code](https://github.com/zyang1580/PDA)] 219 | 1. **Deconfounded Recommendation for Alleviating Bias Amplification**. KDD 2021.[[pdf](https://arxiv.org/pdf/2105.10648.pdf)] [[code](https://github.com/WenjieWWJ/DecRS)] 220 | 1. **Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation**. Arxiv 2021/TKDE 2022.[[pdf](https://arxiv.org/pdf/2109.07946.pdf)] 221 | 1. **Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system**. KDD 2021.[[pdf](https://arxiv.org/pdf/2010.15363.pdf)] [[code](https://github.com/weitianxin/MACR)] 222 | 223 | 1. **Popularity-Opportunity Bias in Collaborative Filtering**. WSDM 2021.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3437963.3441820)] 224 | 225 | 226 | 1. **Multi-sided exposure bias in recommendation**. Arxiv 2020.[[pdf](https://arxiv.org/pdf/2006.15772.pdf)] 227 | 228 | 1. **The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation**. RecSys 2020.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3383313.3418487)] 229 | 230 | 1. **ESAM: discriminative domain adaptation with non-displayed items to improve long-tail performance**. SIGIR 2020.[[pdf](https://arxiv.org/pdf/2005.10545.pdf)] [[code](https://github.com/A-bone1/ESAM.git)] 231 | 1. **Unbiased offline recommender evaluation for missing-not-at-random implicit feedback**. RecSys 2018.[[pdf](https://vision.cornell.edu/se3/wp-content/uploads/2018/08/recsys18_unbiased_eval.pdf)] [[code](https://github.com/ylongqi/unbiased-offline-recommender-evaluation)] 232 | 1. **An adversarial approach to improve long-tail performance in neural collaborative filtering**. CIKM 2018.[[pdf](http://aditk2.web.engr.illinois.edu/reports/sp0781.pdf)] 233 | 234 | 1. **A Probabilistic Reformulation of Memory-Based Collaborative Filtering – Implications on Popularity Biases**. SIGIR 2017[[pdf](https://dl.acm.org/doi/pdf/10.1145/3077136.3080836)] 235 | 236 | 1. **Controlling popularity bias in learning-to-rank recommendation**. RecSys 2017.[[pdf](https://www.researchgate.net/profile/Himan-Abdollahpouri/publication/318351355_Controlling_Popularity_Bias_in_Learning-to-Rank_Recommendation/links/5a1375450f7e9b1e573086d6/Controlling-Popularity-Bias-in-Learning-to-Rank-Recommendation.pdf)] 237 | 1. **Incorporating diversity in a learning to rank recommender system**. FLAIRS 2016.[[pdf](https://aaai.org/papers/572-flairs-2016-12944/)] 238 | 1. **What recommenders recommend: an analysis of recommendation biases and possible countermeasures**. UMUAI 2015.[[pdf](https://link.springer.com/article/10.1007/s11257-015-9165-3)] 239 | 1. **The limits of popularity-based recommendations, and the role of social ties**. KDD 2016.[[pdf](https://arxiv.org/pdf/1607.04263.pdf)] [[code](https://github.com/Steven--/recommender)] 240 | 1. **Correcting popularity bias by enhancing recommendation neutrality**. RecSys 2014.[[pdf](https://www.kamishima.net/archive/2014-po-recsys-print.pdf)] 241 | 1. **Efficiency improvement of neutrality-enhanced recommendation**. RecSys 2013.[[pdf](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.402.6889&rep=rep1&type=pdf#page=5)] [[code](http://www.kamishima.net/inrs/)] 242 | 243 | ### 3.7 Inductive Bias 244 | 1. **Discrete content-aware matrix factorization**. KDD 2017.[[pdf](https://zheng-kai.com/paper/kdd_2017_lian.pdf)] 245 | 1. **Neural collaborative filtering**. WWWW 2017.[[pdf](https://arxiv.org/pdf/1708.05031.pdf?source=post_page---------------------------)] [[code](https://github.com/hexiangnan/neural_collaborative_filtering)] 246 | 1. **Discrete collaborative filtering**. SIGIR 2016.[[pdf](http://staff.ustc.edu.cn/~hexn/papers/sigir16-dcf-cm.pdf)] 247 | 1. **Logistic matrix factorization for implicit feedback data**. NIPS 2014.[[pdf](http://web.stanford.edu/~rezab/nips2014workshop/submits/logmat.pdf)] 248 | 249 | 1. **Learning binary codes for collaborative filtering**. KDD 2012.[[pdf](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.906.4707&rep=rep1&type=pdf)] 250 | 251 | 252 | 253 | 254 | 255 | ### 3.8 Unfairness 256 | 257 | 1. **Providing Previously Unseen Users Fair Recommendations Using Variational Autoencoders**. RecSys 2023.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3604915.3608842)] [[code](https://github.com/BjornarVass/fair-vae-rec)] 258 | 1. **Path-Specific Counterfactual Fairness for Recommender Systems**. KDD 2023.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3580305.3599462)] [[code](https://github.com/yaochenzhu/PSF-VAE)] 259 | 1. **Towards Robust Fairness-aware Recommendation**. Recsys 2023.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3604915.3608784)] 260 | 1. **When Fairness meets Bias a Debiased Framework for Fairness aware Top-N Recommendation**. Recsys 2023.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3604915.3608770)] 261 | 1. **Two-sided Calibration for Quality-aware Responsible Recommendation**. Recsys 2023.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3604915.3608799)] [[code](https://github.com/THUwangcy/ReChorus/tree/RecSys23)] 262 | 1. **Rectifying Unfairness in Recommendation Feedback Loop**. SIGIR 2023.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3539618.3591754)] 263 | 1. **Measuring Item Global Residual Value for Fair Recommendation**. SIGIR 2023.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3539618.3591724)] [[code](https://github.com/Alice1998/TaFR)] 264 | 265 | 1. **Improving Recommendation Fairness via Data Augmentation**. WWW 2023.[[pdf](https://dl.acm.org/doi/10.1145/3543507.3583341)] [[code](https://github.com/newlei/FDA)] 266 | 1. **Controllable Universal Fair Representation Learning**. WWW 2023.[[pdf](https://dl.acm.org/doi/10.1145/3543507.3583307)] 267 | 1. **Cascaded Debiasing: Studying the Cumulative Effect of Multiple Fairness-Enhancing Interventions**. CIKM 2022.[[pdf](https://dl.acm.org/doi/10.1145/3511808.3557155)] [[code](https://github.com/bhavyaghai/Cascaded-Debiasing)] 268 | 269 | 1. **Fighting Mainstream Bias in Recommender Systems via Local Fine Tuning**. WSDM 2022.[[pdf](https://dl.acm.org/doi/10.1145/3488560.3498427)] [[code](https://github.com/Zziwei/Measuring-Mitigating-Mainstream-Bias)] 270 | 1. **CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems**. SIGIR 2022.[[pdf](https://arxiv.org/abs/2204.08085)] [[code](https://github.com/rahmanidashti/CPFairRecSys)] 271 | 1. **Fairness of Exposure in Light of Incomplete Exposure Estimation**. SIGIR 2022.[[pdf](https://arxiv.org/abs/2205.12901)] [[code](https://github.com/MariaHeuss/2022-SIGIR-FOEIncomplete-Exposure)] 272 | 1. **Explainable Fairness in Recommendation**. SIGIR 2022.[[pdf](https://arxiv.org/pdf/2204.11159.pdf)] 273 | 1. **Joint Multisided Exposure Fairness for Recommendation**. SIGIR 2022.[[pdf](https://arxiv.org/pdf/2205.00048.pdf)] [[code](https://github.com/haolun-wu/JMEFairness)] 274 | 1. **Pareto-Optimal Fairness-Utility Amortizations in Rankings with a DBN Exposure Model**. SIGIR 2022.[[pdf](https://arxiv.org/abs/2205.07647)] [[code](https://github.com/naver/expohedron)] 275 | 1. **Optimizing generalized Gini indices for fairness in rankings**. SIGIR 2022.[[pdf](https://arxiv.org/abs/2204.06521)] 276 | 1. **Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking**. SIGIR 2022.[[pdf](https://dl.acm.org/doi/10.1145/3477495.3532045)] [[code](https://github.com/AliVard/PPG)] 277 | 1. **Measuring Fairness in Ranked Outputs**. SIGIR 2022.[[pdf](https://arxiv.org/abs/1610.08559)] 278 | 1. **Comprehensive Fair Meta-learned Recommender System**. KDD 2022.[[pdf](https://arxiv.org/abs/2206.04789)] [[code](https://github.com/weitianxin/CLOVER)] 279 | 1. **Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking**. KDD 2022.[[pdf](https://arxiv.org/abs/2206.07247)] [[code](https://github.com/usaito/kdd2022-fair-ranking-nsw)] 280 | 1. **Fair Representation Learning: An Alternative to Mutual Information**. KDD 2022.[[pdf](https://dl.acm.org/doi/abs/10.1145/3534678.3539302)] [[code](https://github.com/SoftWiser-group/FairDisCo)] 281 | 282 | 1. **Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Users**. WSDM 2021.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3437963.3441769)] [[code](https://github.com/roger-zhe-li/wsdm21-mainstream)] 283 | 284 | 1. **User Bias in Beyond-Accuracy Measurement of Recommendation Algorithms**. RecSys 2021.[[pdf](http://www.comp.hkbu.edu.hk/~lichen/download/Ningxia_RecSys21.pdf)] 285 | 286 | 1. **User-oriented Fairness in Recommendation**. WWW2021.[[pdf](https://arxiv.org/pdf/2104.10671.pdf)] [[code](https://github.com/rutgerswiselab/user-fairness)] 287 | 288 | 1. **Policy-Gradient Training of Fair and Unbiased Ranking Functions**. SIGIR 2021.[[pdf](https://arxiv.org/pdf/1911.08054.pdf)] [[code](https://github.com/him229/fultr)] 289 | 1. **Towards Long-term Fairness in Recommendation**. WSDM 2021.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3437963.3441824)] [[code](https://github.com/TobyGE/FCPO)] 290 | 1. **Towards Personalized Fairness based on Causal Notion**. SIGIR 2021.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3404835.3462966)] 291 | 1. **Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness**. SIGIR 2021.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3404835.3462830)] [[code](https://github.com/HarrieO/2021-SIGIR-plackett-luce)] 292 | 1. **Learning Fair Representations for Recommendation: A Graph-based Perspective**. WWW 2021.[[pdf](https://arxiv.org/pdf/2102.09140.pdf)] [[code](https://github.com/newlei/FairGo)] 293 | 1. **Debiasing Career Recommendations with Neural Fair Collaborative Filtering**. WWW 2021.[[pdf](https://mdsoar.org/bitstream/handle/11603/21218/Islam%20%282021%29%20-%20Debiasing%20Career%20Recommendations%20with%20Neural%20Fair%20Collaborative%20Filtering%20%28WWW%29.pdf?sequence=1&isAllowed=y)] [[code](https://github.com/rashid-islam/nfcf)] 294 | 1. **Debayes: a bayesian method for debiasing network embeddings**. ICML 2020.[[pdf](http://proceedings.mlr.press/v119/buyl20a/buyl20a.pdf)] [[code](https://github.com/1njiku/1njiku.github.io)] 295 | 1. **Measuring and Mitigating Item Under-Recommendation Bias in Personalized Ranking Systems**. SIGIR 2020.[[pdf](https://dl.acm.org/doi/pdf/10.1145/3397271.3401177)] [[code](https://github.com/Zziwei/Item-Underrecommendation-Bias)] 296 | 1. **Controlling fairness and bias in dynamic learning-to-rank**. SIGIR 2020.[[pdf](https://arxiv.org/pdf/2005.14713.pdf)] [[code](https://github.com/MarcoMorik/Dynamic-Fairness)] 297 | 1. **Designing fair ranking schemes**. SIGMOD 2019.[[pdf](https://arxiv.org/pdf/1712.09752.pdf)] 298 | 1. **Fairwalk: Towards fair graph embedding**. IJCAI 2019.[[pdf](https://www.ijcai.org/Proceedings/2019/0456.pdf)] 299 | 1. **Fairness in recommendation ranking through pairwise comparisons**. KDD 2019.[[pdf](https://arxiv.org/pdf/1903.00780.pdf)] 300 | 1. **Compositional fairness constraints for graph embeddings**. ICML 2019.[[pdf](http://proceedings.mlr.press/v97/bose19a/bose19a.pdf)] [[code](https://github.com/joeybose/Flexible-Fairness-Constraints)] 301 | 1. **Fairness-aware ranking in search & recommendation systems with application to linkedin talent search**. KDD 2019.[[pdf](https://arxiv.org/pdf/1905.01989.pdf)] 302 | 1. **Counterfactual fairness: Unidentification bound and algorithm**. IJCAI 2019.[[pdf](https://par.nsf.gov/servlets/purl/10126321)] 303 | 1. **Privacy-aware recommendation with private-attribute protection using adversarial learning**. WSDM 2019.[[pdf](https://arxiv.org/pdf/1911.09872.pdf)] 304 | 305 | 1. **Algorithmic bias? an empirical study of apparent gender-based discrimination in the display of stem career ads**. INFORMS 2019.[[pdf](https://lbsresearch.london.edu/id/eprint/967/1/AlgorithmicBias_Mar2018.pdf)] 306 | 307 | 1. **Crank up the volume: preference bias amplification in collaborative recommendation**. RecSys 2019.[[pdf](https://arxiv.org/pdf/1909.06362.pdf)] 308 | 309 | 1. **Policy Learning for Fairness in Ranking**. NIPS 2019.[[pdf](https://proceedings.neurips.cc/paper/2019/file/9e82757e9a1c12cb710ad680db11f6f1-Paper.pdf)] [[code](https://github.com/ashudeep/Fair-PGRank)] 310 | 1. **Fairness of exposure in rankings**. KDD 2018.[[pdf](https://arxiv.org/pdf/1802.07281.pdf)] 311 | 1. **Fairness-aware tensor-based recommendation**. CIKM 2018.[[pdf](https://par.nsf.gov/servlets/purl/10098220)] [[code](https://github.com/Zziwei/Fairness-Aware_Tensor-Based_Recommendation)] 312 | 1. **Fairness in decision-making - the causal explanation formula**. AAAI 2018.[[pdf](https://ojs.aaai.org/index.php/AAAI/article/view/11564)] 313 | 1. **On discrimination discovery and removal in ranked data using causal graph**. KDD 2018.[[pdf](https://arxiv.org/pdf/1803.01901.pdf)] 314 | 1. **A fairness-aware hybrid recommender system**. FATREC 2018.[[pdf](https://arxiv.org/pdf/1809.09030.pdf)] 315 | 1. **Fair inference on outcomes**. AAAI 2018.[[pdf](https://ojs.aaai.org/index.php/AAAI/article/download/11553/11412)] [[code](https://github.com/raziehna/fair-inference-on-outcomes)] 316 | 317 | 1. **Exploring author gender in book rating and recommendation**. RecSys 2018.[[pdf](https://arxiv.org/pdf/1808.07586.pdf)] [[code](https://github.com/BoiseState/bookdata-tools)] 318 | 319 | 320 | 1. **Homophily influences ranking of minorities in social networks**. Scientific Reports 2018.[[pdf](https://www.nature.com/articles/s41598-018-29405-7.pdf)] 321 | 1. **Algorithmic glass ceiling in social networks: The effects of social recommendations on network diversity**. WWW 2018.[[pdf](http://www.columbia.edu/~as5001/algglassceiling.pdf)] 322 | 323 | 1. **Equity of attention: Amortizing individual fairness in rankings**. SIGIR 2018.[[pdf](https://arxiv.org/pdf/1805.01788.pdf)] 324 | 1. **Fa*ir: A fair top-k ranking algorithm**. CIKM 2017.[[pdf](https://arxiv.org/pdf/1706.06368.pdf)] [[code](https://github.com/MilkaLichtblau/FA-IR_Ranking)] 325 | 1. **Beyond parity: Fairness objectives for collaborative filtering**. NIPS 2017.[[pdf](https://arxiv.org/pdf/1705.08804.pdf)] 326 | 1. **Balanced neighborhoods for fairness-aware collaborative recommendation**. RecSys 2017.[[pdf](https://scholarworks.boisestate.edu/cgi/viewcontent.cgi?article=1002&context=fatrec)] 327 | 1. **Controlling popularity bias in learning-to-rank recommendation**. RecSys 2017.[[pdf](https://www.researchgate.net/profile/Himan-Abdollahpouri/publication/318351355_Controlling_Popularity_Bias_in_Learning-to-Rank_Recommendation/links/5a1375450f7e9b1e573086d6/Controlling-Popularity-Bias-in-Learning-to-Rank-Recommendation.pdf)] 328 | 1. **Considerations on recommendation independence for a find-good-items task**. Recsys 2017.[[pdf](https://scholarworks.boisestate.edu/cgi/viewcontent.cgi?referer=https://scholar.google.com.hk/&httpsredir=1&article=1010&context=fatrec)] 329 | 1. **New fairness metrics for recommendation that embrace differences**. FAT/ML 2017.[[pdf](https://arxiv.org/pdf/1706.09838.pdf)] 330 | 1. **Fairness-aware group recommendation with pareto-efficiency**. RecSys 2017.[[pdf](http://cseweb.ucsd.edu/classes/fa17/cse291-b/reading/p107-xiao.pdf)] 331 | 1. **Counterfactual fairness**. arXiv 2017.[[pdf](https://arxiv.org/pdf/1703.06856.pdf)] [[code](https://github.com/Kaaii/CS7290_Fairness_Eval_Project)] 332 | 1. **Censoring representations with an adversary**. ICLR 2016.[[pdf](https://arxiv.org/pdf/1511.05897.pdf)] 333 | 1. **Model-based approaches for independence-enhanced recommendation**. IEEE 2016.[[pdf](https://www.kamishima.net/archive/2016-ws-icdm-print.pdf)] [[code](http://www.kamishima.net/iers/)] 334 | 335 | 1. **Automated experiments on ad privacy settings: A tale of opacity, choice, and discrimination**. Arxiv 2015.[[pdf](https://arxiv.org/pdf/1408.6491.pdf)] [[code](http://www.cs.cmu.edu/~mtschant/ife/)] 336 | 337 | 1. **Efficiency improvement of neutrality-enhanced recommendation.**. RecSys 2013.[[pdf](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.402.6889&rep=rep1&type=pdf#page=5)] [[code](http://www.kamishima.net/inrs/)] 338 | 1. **Learning fair representations**. JMLR 2013.[[pdf](http://proceedings.mlr.press/v28/zemel13.pdf)] 339 | 1. **Enhancement of the neutrality in recommendation**. RecSys 2012.[[pdf](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.416.1022&rep=rep1&type=pdf#page=12)] 340 | 1. **Discrimination-aware data mining**. KDD 2008.[[pdf](https://dl.acm.org/doi/abs/10.1145/1401890.1401959)] 341 | 1. **Bias in computer systems**. TOIS 1996.[[pdf](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.17.6776&rep=rep1&type=pdf)] 342 | 343 | ### 3.9 Loop Effect 344 | 345 | 1. **Toward Pareto Efficient Fairness-Utility Trade-off inRecommendation through Reinforcement Learning**. WSDM 2022.[[pdf](https://arxiv.org/abs/2201.00140)] 346 | 1. **AutoDebias: Learning to Debias for Recommendation**. SIGIR 2021.[[pdf](https://arxiv.org/pdf/2105.04170.pdf)] [[code](https://github.com/DongHande/AutoDebias)] 347 | 1. **A general knowledge distillation framework for counterfactual recommendation via uniform data**. SIGIR 2020.[[pdf](http://csse.szu.edu.cn/staff/panwk/publications/Conference-SIGIR-20-KDCRec.pdf)] [[code](https://github.com/dgliu/SIGIR20_KDCRec)] 348 | 1. **Influence function for unbiased recommendation**. SIGIR 2020.[[pdf](https://dl.acm.org/doi/abs/10.1145/3397271.3401321)] 349 | 1. **Understanding echo chambers in e-commerce recommender systems**. SIGIR 2020.[[pdf](https://arxiv.org/pdf/2007.02474.pdf)] [[code](https://github.com/szhaofelicia/EchoChamberInEcommerce)] 350 | 351 | 352 | 1. **Jointly learning to recommend and advertise**. KDD 2020.[[pdf](https://arxiv.org/pdf/2003.00097.pdf)] 353 | 1. **Counterfactual evaluation of slate recommendations with sequential reward interactions**. KDD 2020.[[pdf](https://arxiv.org/pdf/2007.12986.pdf)] [[code](https://github.com/spotify-research/RIPS_KDD2020)] 354 | 1. **Joint policy value learning for recommendation**. KDD 2020.[[pdf](http://adrem.uantwerpen.be/bibrem/pubs/JeunenKDD2020.pdf)] [[code](https://github.com/olivierjeunen/dual-bandit-kdd-2020)] 355 | 356 | 1. **Feedback loop and bias amplification in recommender systems**. CIKM 2020.[[pdf](https://arxiv.org/pdf/2007.13019.pdf)] 357 | 358 | 1. **Degenerate feedback loops in recommender systems**. AIES 2019.[[pdf](https://arxiv.org/pdf/1902.10730.pdf)] 359 | 1. **When people change their mind: Off-policy evaluation in non-stationary recommendation environments**. WSDM 2019.[[pdf](https://staff.fnwi.uva.nl/m.derijke/wp-content/papercite-data/pdf/jagerman-when-2019.pdf)] [[code](https://github.com/rjagerman/wsdm2019-nonstationary)] 360 | 1. **Top-k off-policy correction for a reinforce recommender system**. WSDM 2019.[[pdf](https://arxiv.org/pdf/1812.02353.pdf)] [[code](https://github.com/massquantity/DBRL)] 361 | 1. **Improving ad click prediction by considering non-displayed events**. CIKM 2019.[[pdf](https://www.csie.ntu.edu.tw/~cjlin/papers/occtr/ctr_oc.pdf)] [[code](https://www.csie.ntu.edu.tw/~cjlin/papers/occtr/)] 362 | 1. **Large-scale interactive recommendation with tree-structured policy gradient**. AAAI 2019.[[pdf](https://ojs.aaai.org/index.php/AAAI/article/view/4204)] [[code](https://github.com/chenhaokun/TPGR)] 363 | 1. **Deep reinforcement learning for list-wise recommendations**. KDD 2019.[[pdf](https://arxiv.org/pdf/1801.00209.pdf)] [[code](https://github.com/egipcy/LIRD)] 364 | 1. **Causal embeddings for recommendation**. RecSys 2018.[[pdf](https://arxiv.org/pdf/1706.07639.pdf)] [[code](https://github.com/criteo-research/CausE)] 365 | 1. **How algorithmic confounding in recommendation systems increases homogeneity and decreases utility**. RecSys 2018.[[pdf](https://arxiv.org/pdf/1710.11214.pdf)] 366 | 367 | 1. **Stabilizing reinforcement learning in dynamic environment with application to online recommendation**. KDD 2018.[[pdf](https://www.researchgate.net/profile/Qing-Da/publication/324988927_Stablizing_Reinforcement_Learning_in_Dynamic_Environment_with_Application_to_Online_Recommendation/links/5b2b4321aca27209f3797d65/Stablizing-Reinforcement-Learning-in-Dynamic-Environment-with-Application-to-Online-Recommendation.pdf)] 368 | 1. **Recommendations with negative feedback via pairwise deep reinforcement learning**. KDD 2018.[[pdf](https://arxiv.org/pdf/1802.06501.pdf)] 369 | 1. **Drn: A deep reinforcement learning framework for news recommendation**. WWW 2018.[[pdf](http://personal.psu.edu/~gjz5038/paper/www2018_reinforceRec/www2018_reinforceRec.pdf)] 370 | 1. **Deep reinforcement learning for page-wise recommendations**. RecSys 2018.[[pdf](https://arxiv.org/pdf/1805.02343.pdf)] 371 | 1. **A reinforcement learning framework for explainable recommendation**. ICDM 2018.[[pdf](https://www.microsoft.com/en-us/research/uploads/prod/2018/08/main.pdf)] 372 | 1. **Interactive social recommendation**. CIKM 2017.[[pdf](https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=4975&context=sis_research)] 373 | 1. **Off-policy evaluation for slate recommendation**. NIPS 2017.[[pdf](https://arxiv.org/pdf/1605.04812.pdf)] [[code](https://github.com/adith387/slates_semisynth_expts)] 374 | 1. **Factorization bandits for interactive recommendation**. WWW 2016.[[pdf](https://ojs.aaai.org/index.php/AAAI/article/view/10936)] 375 | 1. **Deconvolving feedbackloops in recommender systems**. NIPS 2016.[[pdf](https://arxiv.org/pdf/1703.01049.pdf)] 376 | 377 | 1. **Interactive collaborative filtering**. CIKM 2013.[[pdf](https://discovery.ucl.ac.uk/id/eprint/1401363/1/ir0422-zhao_ucl.pdf)] [[code](https://github.com/h324yang/interactiveCF)] 378 | 1. **A contextual-bandit approach to personalized news article recommendation**. WWW 2010.[[pdf](https://arxiv.org/pdf/1003.0146.pdf)] [[code](https://github.com/akhadangi/Multi-armed-Bandits)] 379 | 380 | 381 | 382 | ### 3.10 Other Bias 383 | 384 | 1. **Counteracting User Attention Bias in Music Streaming Recommendation via Reward Modification**. KDD 2022.[[pdf](https://dl.acm.org/doi/abs/10.1145/3534678.3539393)] 385 | 386 | 1. **Deconfounding Duration Bias inWatch-time Prediction for Video Recommendation**. KDD 2022.[[pdf](https://arxiv.org/abs/2206.06003)] [[code](https://github.com/MorganSQ/Ks-D2Q)] 387 | 388 | 389 | 1. **Causal Intervention for Sentiment De-biasing in Recommendation**. CIKM 2022.[[pdf](https://dl.acm.org/doi/10.1145/3511808.3557558)] 390 | 1. **Mitigating Sentiment Bias for Recommender Systems**. SIGIR 2021.[[pdf](https://dl.acm.org/doi/10.1145/3404835.3462943)] 391 | 392 | 1. **Debiasing Learning based Cross-domain Recommendation**. KDD 2021.[[pdf](https://dl.acm.org/doi/10.1145/3447548.3467067)] 393 | 394 | 395 | 396 | ## Tips 397 | ### We will keep updating this list, and if you find any missing related work or have any suggestions, please feel free to contact us (cjwustc@ustc.edu.cn). 398 | 399 | ### If you find this repository useful to your research or work, it is really appreciate to star this repository. 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