
This research paper introduces IViDR, a novel debiasing method for recommender systems that effectively mitigates dual latent confounding biases stemming from unobserved factors influencing both user-item interactions and item exposure.


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mitigating-dual-latent-confounding-biases-in-recommender-systems-using-instrumental-variables-and-identifiable-variational-auto-encoders-ividr-


Mitigating Dual Latent Confounding Biases in Recommender Systems Using Instrumental Variables and Identifiable Variational Auto-Encoders (IViDR)


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The proposed End-to-End Adaptive Local Learning (TALL) framework effectively counters mainstream bias in recommender systems by addressing the discrepancy modeling problem and the unsynchronized learning problem.


addressing-mainstream-bias-in-recommender-systems-through-end-to-end-adaptive-local-learning


Addressing Mainstream Bias in Recommender Systems through End-to-End Adaptive Local Learning
