  As the recommendation service needs to address increasingly diverse
distributions, such as multi-population, multi-scenario, multitarget, and
multi-interest, more and more recent works have focused on multi-distribution
modeling and achieved great progress. However, most of them only consider
modeling in a single multi-distribution manner, ignoring that mixed
multi-distributions often coexist and form hierarchical relationships. To
address these challenges, we propose a flexible modeling paradigm, named
Hierarchical Multi-Distribution Network (HMDN), which efficiently models these
hierarchical relationships and can seamlessly integrate with existing
multi-distribution methods, such as Mixture of-Experts (MoE) and Dynamic-Weight
(DW) models. Specifically, we first design a hierarchical multi-distribution
representation refinement module, employing a multi-level residual quantization
to obtain fine-grained hierarchical representation. Then, the refined
hierarchical representation is integrated into the existing single
multi-distribution models, seamlessly expanding them into mixed
multi-distribution models. Experimental results on both public and industrial
datasets validate the effectiveness and flexibility of HMDN.
