<ul><li>Recommendation systems face challenges in unbiased learning due to selection bias, leading to distorted user preferences and inaccurate recommendations.</li><li>Various debiasing methods have been developed to address this issue, including error imputation, inverse propensity scoring, and doubly robust techniques.</li><li>A new learning algorithm based on likelihood maximization has been proposed to address correlated latent exogenous variables in recommender systems, moving away from assuming independence of exogenous variables.</li><li>The proposed method handles latent exogenous variables by modeling the data generation process under normality assumptions and using a Monte Carlo algorithm to estimate the likelihood function, showing effectiveness in experiments with synthetic and real-world datasets.</li></ul>

Addressing Correlated Latent Exogenous Variables in Debiased Recommender Systems

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