Hierarchical Invariant Learning for Domain Generalization Recommendation

Most cross-domain recommenders require samples on target domains or source-target overlaps to carry out domain adaptation. However, in many real-world situations, target domains are lack of such knowledge. Few works discuss this problem, whose essence is domain generalization recommendation. In this paper, we figure out domain generalization recommendation with a clear symbolized definition and propose corresponding models. Moreover, we illustrate its strong connection with zero-shot recommendation, pretrained recommendation and cold-start recommendation, distinguishing it from content-based recommendation. By analyzing its properties, we propose HIRL^+ and a series of heuristic methods to solve this problem. We propose hierarchical invariant learning to expel the specific patterns in both domain-level and environment-level, and find the common patterns in generalization space. To make the division of environments flexible, fine-grained and balanced, we put forward a learnable environment assignment method. To improve the robustness against distribution shifts inside domain generalization, we present an adversarial environment refinement method. In addition, we conduct experiments on real-word datasets to verify the effectiveness of our models, and carry out further studies on the domain distance and domain diversity. To benefit the research community and promote this direction, we discuss the future of this field.