  In sponsored search it is critical to match ads that are relevant to a query
and to accurately predict their likelihood of being clicked. Commercial search
engines typically use machine learning models for both query-ad relevance
matching and click-through-rate (CTR) prediction. However, matching models are
based on the similarity between a query and an ad, ignoring the fact that a
retrieved ad may not attract clicks, while click models rely on click history,
being of limited use for new queries and ads. We propose a deeply supervised
architecture that jointly learns the semantic embeddings of a query and an ad
as well as their corresponding CTR.We also propose a novel cohort negative
sampling technique for learning implicit negative signals. We trained the
proposed architecture using one billion query-ad pairs from a major commercial
web search engine. This architecture improves the best-performing baseline deep
neural architectures by 2\% of AUC for CTR prediction and by statistically
significant 0.5\% of NDCG for query-ad matching.
