Causally Attentive Collaborative Filtering

Abstract

Attention-based recommender models hold the promise of improving performance by learning to discriminate different user/item feature importances. However, due to the existence of the latent confounders, the correlations captured by attention mechanisms may fail to reflect the true influence of the features on the targets (i.e., spurious correlation). In this paper, we propose to empower attention mechanism by the causal inference, which is a powerful tool to identify the real causal effects. Our model is based on the potential outcome framework, where the item features are regarded as the treatment and the outcome is the predicted user preference. In specific, the causal relation of each feature on the outcome is measured by the individual treatment effect (ITE). In order to distill the causal information into the attention learning process, we minimize the distance between the traditional attention weights and the normalized ITE. With such causal regularization, the learned attention weights can capture the real causal effects, which are expected to correct the feature importances for improving performance. We conduct extensive experiments based on three real-world datasets to demonstrate the effectiveness.