Apr 25, 2022 · In this paper, we seek to solve the problem of selection bias from a novel perspective. Recommendation models are typically trained via ...

To break these limitations, in this work, we propose a novel self-supervised learning (SSL) framework, i.e., Rating Distribution Calibration (RDC), to tackle ...

Rating Distribution Calibration for Selection Bias Mitigation in Recommendations ... It develops dual-debiased learning to mitigate selection bias and exposure ...

Duration: 21:38
Posted: Sep 12, 2022

“Rating Distribution Calibration for Selection Bias Mitigation in Recommendations” is a paper by Haochen Liu Da Tang Jing Yang Xiangyu Zhao Hui Liu Jiliang ...

Aug 16, 2021 · In this work, we propose a novel self-supervised learning (SSL) framework Rating Distribution. Calibration (RDC) to alleviate the negative ...

Oct 25, 2023 · Data imputation estimates and gen- erates pseudo-labels or ratings for users' unseen items, aiming to mitigate the selection bias, result- ing ...

Rating Distribution Calibration for Selection Bias Mitigation in Recommendations ... Self-supervised Learning for Alleviating Selection Bias in Recommendation ...

This work proposes a novel counterfactual contrastive learning framework for recommendation, named CounterCLR, to tackle the problem of non-random missing ...