  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.
