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- Title
Deeply supervised model for click-through rate prediction in sponsored search.
- Authors
Gligorijevic, Jelena; Gligorijevic, Djordje; Stojkovic, Ivan; Bai, Xiao; Goyal, Amit; Obradovic, Zoran
- Abstract
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.
- Subjects
WEB search engines; MACHINE learning
- Publication
Data Mining & Knowledge Discovery, 2019, Vol 33, Issue 5, p1446
- ISSN
1384-5810
- Publication type
Article
- DOI
10.1007/s10618-019-00625-3