Using boosted trees for click-through rate prediction for sponsored search

Ilya Trofimov, Anna Kornetova, Valery Topinskiy

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

21 Citations (Scopus)

Abstract

We describe a new approach to solving the click-through rate (CTR) prediction problem in sponsored search by means of MatrixNet, the proprietary implementation of boosted trees. This problem is of special importance for the search engine, because choosing the ads to display substantially depends on the predicted CTR and greatly affects the revenue of the search engine and user experience. We discuss different issues such as evaluating and tuning MatrixNet algorithm, feature importance, performance, accuracy and training data set size. Finally, we compare MatrixNet with several other methods and present experimental results from the production system.

Original languageEnglish
Title of host publicationProceedings of the 6th International Workshop on Data Mining for Online Advertising and Internet Economy, ADKDD'12
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event6th International Workshop on Data Mining for Online Advertising and Internet Economy, ADKDD 2012 - Beijing, China
Duration: 12 Aug 201216 Aug 2012

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference6th International Workshop on Data Mining for Online Advertising and Internet Economy, ADKDD 2012
Country/TerritoryChina
CityBeijing
Period12/08/1216/08/12

Keywords

  • Boosting
  • Click-through rate
  • CTR
  • Decision tree
  • Sponsored search
  • Web advertising

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