Debiased Recommendation

Debiased recommendation aims to mitigate biases in recommender systems that skew results towards popular items or reflect existing societal inequalities, ultimately improving recommendation accuracy and user satisfaction. Current research focuses on leveraging causal inference techniques, such as instrumental variables and doubly robust methods, along with contrastive learning and propensity scoring, to create unbiased models. These approaches often incorporate neural networks and autoencoders to learn user and item representations, disentangle confounding factors, and estimate counterfactual outcomes. The development of robust debiasing techniques is crucial for building fairer and more effective recommender systems across various applications.