Summary of A Semi-supervised Multi-channel Graph Convolutional Network For Query Classification in E-commerce, by Chunyuan Yuan et al.
A Semi-supervised Multi-channel Graph Convolutional Network for Query Classification in E-commerce
by Chunyuan Yuan, Ming Pang, Zheng Fang, Xue Jiang, Changping Peng, Zhangang Lin
First submitted to arxiv on: 4 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 In this research paper, the authors address the issue of category imbalance in query intent classification on e-commerce platforms. Most existing methods rely on user click behavior as a training signal, but this can lead to serious imbalances in popular versus long-tail categories. The authors propose a novel approach that addresses these issues and improves recall for long-tail categories. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about helping people find what they’re looking for online more easily. Right now, most methods for figuring out what someone wants rely on what they click on. But this can be unfair to things that aren’t as popular, making it hard for those products to get noticed. The authors are trying to fix this problem by coming up with a new way of doing things. |
Keywords
» Artificial intelligence » Classification » Recall
