JSM 2015 Online Program

Online Program Home
My Program

Abstract Details

Activity Number: 41
Type: Contributed
Date/Time: Sunday, August 9, 2015 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #315687
Title: Comparison of R and Vowpal Wabbit for Click Prediction in Display Advertising
Author(s): Jaimyoung Kwon* and Bin Ren and Rajasekhar Cherukuri and Marius Holtan
Companies: AOL Advertising and AOL Platforms and AOL Platforms and AOL Platforms
Keywords: click prediction ; display advertising ; lasso ; R ; Vowpal Wabbit ; Hadoop

Efficient operation of online display advertising campaigns relies on accurate prediction of the likelihood that a web user clicks on a particular banner on a particular site. The problem, which is one of canonical examples of the application of machine learning and statistics to the "big data", is essentially a classification problem with many predictor variables, where petabytes of input data are processed in a Hadoop pipeline. Commonly applied methods include lasso, random forest, and gradient boosting, implemented on such computing platforms as R, Vowpal Wabbit, and native implementation. Among these, Vowpal Wabbit, an out-of-core learning system, has become a popular choice in many large scale machine learning problems. In this paper, we compare the performance of a few different algorithms and platform combinations. In particular, we compare the performance of algorithms implemented in R and Vowpal Wabbit in various sample size and feature number combinations, and their interactions with sampling, feature selection, and feature transformation.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2015 program

For program information, contact the JSM Registration Department or phone (888) 231-3473.

For Professional Development information, contact the Education Department.

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

2015 JSM Online Program Home