HMDN：用于点击率预测的层次化多分布网络
HMDN: Hierarchical Multi-Distribution Network for Click-Through Rate Prediction
AI总结：
针对推荐中多种多分布共存且呈层次关系的问题，提出 HMDN，通过多级残差量化精炼层次表示并扩展现有 MoE、DW 等模型，实验验证其有效且灵活。
摘要：
随着推荐服务需要处理日益多样化的分布，例如多种群、多场景、多目标和多兴趣，越来越多的近期工作开始关注多分布建模并取得了显著进展。然而，其中大多数方法只考虑以单一的多分布方式进行建模，忽略了混合多分布往往同时存在并形成层次关系。为应对这些挑战，我们提出了一种灵活建模范式，名为 Hierarchical Multi-Distribution Network（HMDN，层次化多分布网络），可高效建模这些层次关系，并能与 Mixture-of-Experts（MoE）和 Dynamic-Weight（DW）模型等现有多分布方法无缝集成。具体而言，我们首先设计了一个层次化多分布表示精炼模块，采用多级残差量化以获得细粒度的层次化表示。随后，将精炼后的层次化表示集成到现有单一多分布模型中，将其无缝扩展为混合多分布模型。在公开数据集和工业数据集上的实验结果验证了 HMDN 的有效性和灵活性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
