graph neural networks

• Improving Query Categorization with Categorical Graph Neural Networks

  BigData (Industry) 2021

  Tianchuan Du, Keng-Hao Chang, Paul Liu, Ruofei Zhang

  ---

  

  We propose a class of graph-based network structures, which we call Categorical Graph Neural Networks (CaGNN). Over a query categorization dataset of 2k categories and another ad title categorization dataset of 5k categories, CaGNN improves performance significantly compared to a baseline Deep Neural Network model without the CaGNN structure. Notably, top 3 prediction recall increases from 90.15% to 91.40% for the ad title categorization task, for which is quite significant at over 90% level for more than 5k categories. By inspecting the learned category embeddings and the flow of message passing, we show that CaGNN effectively encapsulates useful graph structural information. Online A/B testing result shows that an ad ranking model with CaGNN-based features has increased ad click-through rate by 1.81% and reduced defect rate by 2.64%. The model has been deployed to production.

  graph neural networks machine learning

  [pdf]