Model-Agnostic Causal Embedding Learning for Counterfactually Group-Fair Recommendation

Group-fair recommendation aims at ensuring the equality of recommendation results across user groups categorized by sensitive attributes (e.g., gender, occupation, etc.). Existing group-fair recommendation models traditionally employ original user embeddings for both training and testing, primarily focusing on statistical learning while imposing group fairness constraints under the I.I.D. assumption. However, these models encounter limitations when addressing out-of-distribution (OOD) sensitive attributes. The fundamental issue of unfairness within user embeddings arises from a causal perspective, where each embedding vector comprises an exogenous component devoid of correlations with sensitive attributes and an endogenous component strongly correlated with these attributes. Overlooking the distinction between these two components during model training renders models sensitive to shifts in the distribution of sensitive attributes. This paper introduces the concept of Counterfactual Group Fairness (CGF) along with a corresponding metric to evaluate group fairness in scenarios involving OOD sensitive attributes in recommender systems. Building on this foundation, we propose a model-agnostic causal embedding learning framework named MACE. MACE effectively disentangles user embedding vectors into their exogenous and endogenous parts, thus ensuring group fairness, even in the presence of OOD sensitive attributes in embeddings. Specifically, MACE identifies the exogenous part of each user's embedding using mutual information minimization, treating it as instrumental variables. Subsequently, under the constraint of CGF, MACE reconstructs the endogenous and exogenous parts using the instrumental variable regression, combines the obtained parts into novel user embeddings using deep neural networks, and uses the combined embeddings for fair recommendation. Experimental results demonstrated that MACE can outperform the state-of-the-art baselines in terms of the metric of CGF while maintaining a comparable recommendation accuracy.