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Deeply supervised model for click-through rate prediction in sponsored search
Gligorijevic, J Gligorijevic, D Stojkovic, I Bai, X Goyal, A Obradovic, Z
Gligorijevic, J
Gligorijevic, D
Stojkovic, I
Bai, X
Goyal, A
Obradovic, Z
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Pre-print
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2019-09-01
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10.1007/s10618-019-00625-3
Abstract
© 2019, The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature. 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, limiting their 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.
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Data Mining and Knowledge Discovery
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