Applying ML & NLP in Google Ads
Building and deploying machine-learning (ML) models at Google comes with interesting challenges. For example, some models have to handle massive amounts of training data, while some supervised tasks have insufficient amount of training labels. Or, even when the model quality is good enough for a product requirement, it may not meet other requirements (e.g., serving latency, memory footprint). In this talk we will discuss some of these challenges and share our experiences from deploying ML models for quality improvements in Search Ads products via some case studies. One particular case study I will discuss in detail is a recent paper where we use deep neural networks to understand ad performance and attribute it to particular parts of ad text. This is an interesting research problem in Natural Language Processing (NLP) -- we will outline our key results related to this problem, and discuss interesting areas of future research.
Dr. Sugato Basu is currently the Tech Lead of the AdsAI team in Google, which applies state-of-the-art machine learning (ML) and natural language processing (NLP) technology to challenging problems in Search Ads at Google. He joined Google in 2007 and has worked for more than a decade on various ML problems in computational advertising related to the prediction stack, auction, pricing, and user/advertiser modeling. Before that he spent about a year at SRI International working on the CALO project, a precursor to Siri. Sugato did his PhD in Computer Science from the University of Texas at Austin, where his research on semi-supervised clustering got 2 best paper awards.