2024/08/02 by Xingyu Lou, Yang Yu, Lou, Xingyu +12
Computer Science · Social Sciences · #Advanced Computing and Algorithms #FOS: Computer and information sciences #Image and Video Quality Assessment #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2408.01332
openalex publication_date 2024/08/02 · openalex created_date 2025/01/13 · openalex updated_date 2026/07/28
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.