HMDN: Hierarchical Multi-Distribution Network for Click-Through Rate Prediction
<|reference_start|>HMDN: Hierarchical Multi-Distribution Network for Click-Through Rate Prediction: 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.<|reference_end|>
@article{lou2024hmdn:,
    title={HMDN: Hierarchical Multi-Distribution Network for Click-Through Rate
  Prediction},
    author={Xingyu Lou, Yu Yang, Kuiyao Dong, Heyuan Huang, Wenyi Yu, Ping Wang,
  Xiu Li, Jun Wang},
    journal={arXiv preprint arXiv:2408.01332},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01332},
    primaryClass={cs.LG}
}