Bias Fairness

Zzong's Notes

검색과 추천 모델을 만드는 머신러닝 엔지니어가 일하고 공부하며 정리한 노트입니다. 개념 설명, 논문 읽은 기록, 직접 해 본 실험을 한곳에 모았습니다. 쓴 사람이 궁금하다면 이력서를 보세요. 처음이라면 여기부터 검색이 궁금하다면 검색 노트 안내 단어로 찾는 방법과 뜻으로 찾는 방법이 어떻게 다른지부터 짚고, 나머지 검색 노트로 이어 줍니다. LLM 학습이 궁금하다면 자연스러운 한국어를 위한 ...

Zzong's Notes

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Bias Fairness

Bias and Debias in Recommender System - A Survey and Future Directions

Calibrated Recommendations

Causal Inference for Recommendation

Causal Inference for Recommender Systems

Clicks can be Cheating - Counterfactual Recommendation for Mitigating Clickbait Issue

Combating Selection Biases in Recommender Systems with a Few Unbiased Ratings

Correcting for Selection Bias in Learning-to-rank Systems

Debiasing the Human-Recommender System Feedback Loop in Collaborative Filtering

Denoising Implicit Feedback for Recommendation

Doubly robust joint learning for recommendation on data missing not at random

Fighting Fire with Fire - Using Antidote Data to Improve Polarization and Fairness of Recommender Systems

Mitigating Confounding Bias in Recommendation via Information Bottleneck

Modeling user exposure in recommendation

Popularity Bias in Dynamic Recommendation

Popularity-Opportunity Bias in Collaborative Filtering

Position bias estimation for unbiased learning to rank in personal search

Recommendations as treatments - debiasing learning and evaluation

Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations

The Limits of Popularity-Based Recommendations, and the Role of Social Ties

The Unfairness of Popularity Bias in Recommendation

When Inverse Propensity Scoring does not Work - Affine Corrections for Unbiased Learning to Rank

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