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Modern Models for Learning Large-Sca...

Zhang, Qiang.

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Modern Models for Learning Large-Scale Highly Skewed Online Advertising Data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modern Models for Learning Large-Scale Highly Skewed Online Advertising Data./
Author:	Zhang, Qiang.
Description:	39 p.
Notes:	Source: Masters Abstracts International, Volume: 54-04.
Contained By:	Masters Abstracts International54-04(E).
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1585323
ISBN:	9781321622065

Modern Models for Learning Large-Scale Highly Skewed Online Advertising Data.
Zhang, Qiang.

Modern Models for Learning Large-Scale Highly Skewed Online Advertising Data. - 39 p.

Source: Masters Abstracts International, Volume: 54-04.

Thesis (M.S.)--University of California, Los Angeles, 2015.

Click through rate (CTR) and conversation rate estimation are two core prediction tasks in online advertising. However, four major challenges emerged as data scientists trying to analyze the advertising data - sheer volume, the amount of data available for mining is massive; complex structure, there is no easy way to tell what factors drive a user to click an ad or make a conversion and how the factors interacted with one another; high cardinality for categorical variables, features like device id usually have tons of possible values which will lead to very sparse data; severe skewness in response variable with the majority of the users not clicking the ad. In this paper, I will make a comprehensive summary of the state-of-art machine learning models (decision tree based, regularized logistic regression, online learning, and factorization machine) that are often used in the industry to solve the problem. Insights and practical tricks are then provided based on a wide range of experiments conducted on multiple data sets with different characteristics.

ISBN: 9781321622065Subjects--Topical Terms:

517247
Statistics.

Modern Models for Learning Large-Scale Highly Skewed Online Advertising Data.
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245 1 0 $a Modern Models for Learning Large-Scale Highly Skewed Online Advertising Data.
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500 $a Source: Masters Abstracts International, Volume: 54-04.
500 $a Adviser: Ying Nian Wu.
502 $a Thesis (M.S.)--University of California, Los Angeles, 2015.
520 $a Click through rate (CTR) and conversation rate estimation are two core prediction tasks in online advertising. However, four major challenges emerged as data scientists trying to analyze the advertising data - sheer volume, the amount of data available for mining is massive; complex structure, there is no easy way to tell what factors drive a user to click an ad or make a conversion and how the factors interacted with one another; high cardinality for categorical variables, features like device id usually have tons of possible values which will lead to very sparse data; severe skewness in response variable with the majority of the users not clicking the ad. In this paper, I will make a comprehensive summary of the state-of-art machine learning models (decision tree based, regularized logistic regression, online learning, and factorization machine) that are often used in the industry to solve the problem. Insights and practical tricks are then provided based on a wide range of experiments conducted on multiple data sets with different characteristics.
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650 4 $a Marketing. $3 536353
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