Curriculum Co-disentangled Representation Learning across Multiple Environments for Social Recommendation

There exist complex patterns behind the decisionmaking processes of different individuals across different environments. For instance, in a social recommender system, various user behaviors are driven by highly entangled latent factors from two environments, i.e., consuming environment where users consume items and social environment where users connect with each other. Uncovering the disentanglement of these latent factors for users can benefit in enhanced explainability and controllability for recommendation. However, in literature there has been no work on social recommendation capable of disentangling user representations across consuming and social environments. To solve this problem, we study codisentangled representation learning across different environments via proposing the curriculum codisentangled representation learning (CurCoDis) model to disentangle the hidden factors for users across both consuming and social environments. To co-disentangle joint representations for useritem consumption and user-user social graph simultaneously, we partition the social graph into equal-size sub-graphs with minimum number of edges being cut, and design a curriculum weighing strategy for subgraph training through measuring the complexity of subgraphs via Descartes' rule of signs. We further develop the prototyperouting optimization mechanism, which achieves co-disentanglement of user representations across consuming and social environments. Extensive experiments for social recommendation demonstrate that our proposed CurCoDis model can significantly outperform state-of-the-art methods on several real-world datasets.