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Real-Time Data Analytics and Optimization for Computational Advertising

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Date Issued:
2017
Summary:
Online advertising has built a market of hundreds of billions of dollars and still continues to grow. With well developed techniques in big data storage, data mining and analytics, online advertising is able to reach targeted audiences e ctively. Real- time bidding refers to the buying and selling of online ad impressions through ad inventory auctions which occur in the time it takes a webpage to load. How to de- termine the bidding price and how to allocate the budget of advertising is the key to successful ad campaigns. Both of these aspects are fundamental to most campaign optimizations and we will introduce both of them in this thesis. For bidding price determination, we improved the estimation of CTR (Click Through Rate) (one of the most important factors of determining the bidding price) by using a re ned hierar- chical tree structure for the estimation. The result of the experiment and the A/B test showed our proposal can provide stable improvement. For budget allocation, we introduce SCO (Single Campaign Optimization) and CCO (Cross Campaign Opti- mization). SCO has been applied by our commercial partner while CCO needs more research. We will rst introduce the methods of SCO and then give our proposal about CCO. We modeled CCO as a LP (Linear Programming) problem as well as designed an e ective procedure to implement optimal impressions distribution. Our simulation showed our proposal can signi cantly increase global Gross Pro t (GP).
Title: Real-Time Data Analytics and Optimization for Computational Advertising.
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Name(s): Liu, Hui, author
Zhu, Xingquan, Thesis advisor
Florida Atlantic University, Degree grantor
College of Engineering and Computer Science
Department of Computer and Electrical Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Created: 2017
Date Issued: 2017
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 71 p.
Language(s): English
Summary: Online advertising has built a market of hundreds of billions of dollars and still continues to grow. With well developed techniques in big data storage, data mining and analytics, online advertising is able to reach targeted audiences e ctively. Real- time bidding refers to the buying and selling of online ad impressions through ad inventory auctions which occur in the time it takes a webpage to load. How to de- termine the bidding price and how to allocate the budget of advertising is the key to successful ad campaigns. Both of these aspects are fundamental to most campaign optimizations and we will introduce both of them in this thesis. For bidding price determination, we improved the estimation of CTR (Click Through Rate) (one of the most important factors of determining the bidding price) by using a re ned hierar- chical tree structure for the estimation. The result of the experiment and the A/B test showed our proposal can provide stable improvement. For budget allocation, we introduce SCO (Single Campaign Optimization) and CCO (Cross Campaign Opti- mization). SCO has been applied by our commercial partner while CCO needs more research. We will rst introduce the methods of SCO and then give our proposal about CCO. We modeled CCO as a LP (Linear Programming) problem as well as designed an e ective procedure to implement optimal impressions distribution. Our simulation showed our proposal can signi cantly increase global Gross Pro t (GP).
Identifier: FA00004940 (IID)
Degree granted: Thesis (M.S.)--Florida Atlantic University, 2017.
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): Includes bibliography.
Subject(s): Internet marketing--Technological innovations.
Internet advertising--Technological innovations.
Data mining.
Web usage mining.
Business--Data processing.
Held by: Florida Atlantic University Libraries
Sublocation: Digital Library
Links: http://purl.flvc.org/fau/fd/FA00004940
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00004940
Use and Reproduction: Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.