Graphite：一种用于关键短语推荐的基于图的极端多标签短文本分类器
Graphite: A Graph-based Extreme Multi-Label Short Text Classifier for Keyphrase Recommendation
AI总结：
提出基于图的轻量级分类器Graphite，解决广告电商中实时关键短语推荐问题，无需GPU即可在超大规模数据集上训练，性能媲美标准模型且更具可解释性。
摘要：
关键短语推荐一直是广告和电子商务中的关键问题，系统向广告商/卖家推荐关键短语（搜索查询）以供其竞价，从而增加销售额。由于在线平台上展示的商品繁多，且用户搜索的查询多种多样，同时用户对展示商品表现出不同的兴趣，这使得该任务极具挑战性。此外，查询/关键短语推荐必须在实时且资源受限的环境中进行。通过将输入文本标记关键词作为标签，该问题可被构建为极端多标签（XML）短文本分类问题。由于庞大的标签空间，传统的神经网络模型要么不可行，要么推理延迟较高。我们提出了Graphite，一种基于图的分类器模型，能提供与标准文本分类模型相当且实时的关键短语推荐。此外，它不使用GPU资源，这在生产环境中往往是受限的。得益于其轻量级特性和较小的资源占用，它能够在超大规模数据集上进行训练，而最先进的XML模型则会因极高的资源需求而失败。Graphite是确定性的、透明的，并且本质上比基于神经网络的模型更具可解释性。我们对模型在eBay英语站点的40个类别上的性能进行了全面分析。
英文摘要：
Keyphrase Recommendation has been a pivotal problem in advertising and e-commerce where advertisers/sellers are recommended keyphrases (search queries) to bid on to increase their sales. It is a challenging task due to the plethora of items shown on online platforms and various possible queries that users search while showing varying interest in the displayed items. Moreover, query/keyphrase recommendations need to be made in real-time and in a resource-constrained environment. This problem can be framed as an Extreme Multi-label (XML) Short text classification by tagging the input text with keywords as labels. Traditional neural network models are either infeasible or have slower inference latency due to large label spaces. We present Graphite, a graph-based classifier model that provides real-time keyphrase recommendations that are on par with standard text classification models. Furthermore, it doesn't utilize GPU resources, which can be limited in production environments. Due to its lightweight nature and smaller footprint, it can train on very large datasets, where state-of-the-art XML models fail due to extreme resource requirements. Graphite is deterministic, transparent, and intrinsically more interpretable than neural network-based models. We present a comprehensive analysis of our model's performance across forty categories spanning eBay's English-speaking sites.
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。
