Enhancing collaborative translational metric learning with causal graph network

The application of metric learning in recommender systems has gained considerable attention, particularly for enabling supervised training through Euclidean distance. However, conventional collaborative translational metric learning typically represents each user and item as a single point and projects collaborative signals onto a specific translation vector within a uniform space. This approach fails to explicitly capture the high-order semantics and dynamic nature of user-item interactions derived from implicit user behavior. Given the extensive research on graph neural networks (GNNs), a natural extension is to incorporate high-order information into the generation of translation vectors. Yet, our empirical findings reveal that the performance is not as expected. In this paper, we systematically investigate the limitations of existing methods and attribute the issue to semantic confusion. Motivated by this finding, we propose a novel framework that integrates bilateral metric learning with a causal graph network. Specifically, we construct a structural causal model (SCM) to disentangle user behavior into causal features and confounders. To mitigate the influence of these confounders, we propose a conditional intervention module that generates confounder-specific conditions for the target. Moreover, inspired by this conditional intervention mechanism, we develop an additional module conditioned on user behavior to enhance the diversity of the recommended list, recognizing that diversity is also a crucial metric for evaluating recommendation quality. We conducted a series of experiments to validate the effectiveness of our proposed method. The results demonstrate that our approach achieves state-of-the-art performance in terms of both recommendation accuracy and diversity.