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.

翻译：关键词推荐一直是广告和电子商务领域的关键问题，广告主/卖家通过被推荐关键词（搜索查询）进行竞价以提高销售额。由于在线平台展示的商品种类繁多，且用户搜索的查询多种多样，同时对展示商品的兴趣各异，该任务极具挑战性。此外，查询/关键词推荐需要在资源受限的环境中实时完成。该问题可被构建为极端多标签短文本分类任务，即用关键词作为标签对输入文本进行标注。传统神经网络模型因标签空间过大而不可行或推理延迟较高。我们提出Graphite，一种基于图的分类器模型，能够提供与标准文本分类模型性能相当的关键词实时推荐。此外，该模型无需使用GPU资源，这在生产环境中可能受限。由于其轻量级特性和较小的资源占用，该模型能够训练超大规模数据集，而当前最先进的极端多标签模型因资源需求过高而无法处理此类数据。Graphite具有确定性、透明性，且本质上比基于神经网络的模型更具可解释性。我们通过对eBay英语站点四十个类别的全面分析，展示了模型的性能表现。