Zzong's Notes

방문

❯

❯

❯

The Unfairness of Popularity Bias in Recommendation

The Unfairness of Popularity Bias in Recommendation

최초 작성 2026-06-14마지막 수정 2026-08-29읽는 데 1분

노트 편집 ↗

함께 보면 좋은 글

Popularity Bias in Dynamic Recommendation

기존의 모든 popularity bias 연구들은 static 추천 task 를 기반으로 진행했지만, 실제 real-world 에서는 dynamic recommendation 형태를 띈다.

Popularity-Opportunity Bias in Collaborative Filtering

popularity-opportunity bias(POB) 를 조사하기 위해 세 파트로 나눠서 연구를 진행함

Calibrated Recommendations

Reference paper: https://dl.acm.org/doi/pdf/10.1145/3240323.3240372 cailbration 의 목적 In the recommended list of items, calibration ensures that the various (past) areas of...

Correcting for Selection Bias in Learning-to-rank Systems

selection bias, which occurs because clicked documents are reflective of what documents have been shown to the user in the first place. propose new counterfactual approaches...

Debiasing the Human-Recommender System Feedback Loop in Collaborative Filtering

Tags: paper review, matrix factorization , WWW 19

Recommendations as treatments - debiasing learning and evaluation

추천 시스템에서 학습 또는 평가를 위한 데이터는 대부분 selection bias 에 취약하다.

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

Blindly fitting the data without considering the inherent biases will result in many serious issues, e.g., the discrepancy between offline evaluation and online metrics,...

Combating Selection Biases in Recommender Systems with a Few Unbiased Ratings

Tags selection bias, Abstract Introduction unbiased ratings 포함시키는 방법: 임의로 선택된 일부 아이템들에 대해 유저들에게 rating 을 요청함 Related work Recommendation Debiasing 기존의 debiasing 은 주로 두 가지 측면에...

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

추천 시스템의 사회적 desirability 을 향상시키기 위한 아이디어를 제공: 입력 데이터에 추가적인 데이터 (해독 데이터) 를 입력

Causal Inference for Recommendation

Joint models of exposure and clicks