Paper Info
Title
Deeply Supervised Semantic Model for Click-Through Rate Prediction in Sponsored Search.
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, 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.
Year
Venue
Field
2018
arXiv: Information Retrieval
Web search engine,Learning to rank,Click-through rate,Architecture,Search engine,Information retrieval,Computer science,Sampling (statistics),Semantic data model
DocType
Volume
Citations 
Journal
abs/1803.10739
0
PageRank 
References 
Authors
0.34
21
6
Name
Order
Citations
PageRank
Jelena Gligorijevic152.16
Djordje Gligorijevic2196.55
Ivan Stojkovic300.34
Xiao Bai4151.59
Amit Goyal54413.24
Zoran Obradovic61110137.41