Multilingual Taxonomic Web Page Classification for Contextual Targeting at Yahoo

As we move toward the cookie-less world, the ability to track users' online activities for behavior targeting will be drastically reduced, making contextual targeting an appealing alternative for advertising platforms to grow their business. Category-based contextual targeting displays ads on web pages that are relevant to advertiser-targeted categories, according to a pre-defined taxonomy. Accurate web page classification is key to the success of this approach. In this paper, we use multilingual Transformer-based transfer learning models to classify web pages in five high-impact languages. We adopt a number of data sampling techniques to increase coverage for rare categories, and modify the loss using class-based re-weighting to smooth the influence of frequent versus rare categories. Offline evaluation shows that these are crucial to improving our classifiers. We leverage knowledge distillation to train accurate models that are lightweight in terms of (i) model size, and (ii) the input text used. Classifying web pages using only text from the URL addresses a unique challenge for contextual targeting in that bid requests come to ad systems as URLs without associated content, while crawling is very time consuming and costly. We launched the proposed models for contextual targeting in the Yahoo DSP, which significantly increases its revenue share compared to existing targeting strategies.