Summary of Hmdn: Hierarchical Multi-distribution Network For Click-through Rate Prediction, by Xingyu Lou et al.
HMDN: Hierarchical Multi-Distribution Network for Click-Through Rate Prediction
by Xingyu Lou, Yu Yang, Kuiyao Dong, Heyuan Huang, Wenyi Yu, Ping Wang, Xiu Li, Jun Wang
First submitted to arxiv on: 2 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Hierarchical Multi-Distribution Network (HMDN) addresses the challenges of modeling diverse distributions by efficiently representing hierarchical relationships between multiple populations, scenarios, targets, and interests. This paradigm seamlessly integrates with existing single multi-distribution models like Mixture of-Experts (MoE) and Dynamic-Weight (DW) models. The HMDN consists of a hierarchical representation refinement module that employs multi-level residual quantization to obtain fine-grained representations. These refined representations are then integrated into the existing models, expanding them to handle mixed multi-distribution scenarios. Experimental results on public and industrial datasets demonstrate the effectiveness and flexibility of HMDN. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Hierarchical Multi-Distribution Network (HMDN) is a new way for computers to understand complex data distributions. Currently, most recommendation systems can only handle one type of distribution, like people with similar interests or preferences. However, in real life, there are many types of distributions that exist together and have relationships with each other. The HMDN helps address this challenge by creating a model that can represent these hierarchical relationships. This allows the model to be more flexible and work well on different datasets. The results show that HMDN is effective and can be used in real-world applications. |
Keywords
* Artificial intelligence * Mixture of experts * Quantization
