Deconfounded recommendation via causal intervention

Traditional recommenders suffer from hidden confounding factors, leading to the spurious correlations between user/item profiles and user preference prediction, i.e., the confounding bias issue. Most works resort to only one confounding bias, which greatly block their applications on recommendations with mixture confounder, i.e., more than one bias. It is therefore of practical demand to empower the recom-mender with the capability of debiasing different biases from data. Moreover, the positive effect of bias is neglected in most previous works. We argue that confounding bias is actually beneficial for capturing users' preferences in some recommendation scenarios. In this paper, we propose a novel deconfounded causal learning method called GCRec (Graph Causal Recommendetion) to debias two confounders: social network confounder and item group confounder. We employ Graph Neural Networks (GNNs) to aggre-gate user-user connections for social networks and user-item interactions for item groups in order to learn high-order representations that can efficiently debias these two confounders from a causal view. In the inference stage, we use symmetric Kullback-Leibler divergence to measure the user preference drift. If the divergence is large, we perform the causal intervention to alleviate the bias amplification caused by confounders on user preferences. Otherwise, we incorporate the user preferences that can potentially deliver a positive effect on favoring recommendation performance. Extensive experiments are conducted on two benchmark datasets to verify that GCRec outperforms state-of-the-art methods and achieves robust recommendations. (c) 2023 Published by Elsevier B.V.