SCHEDULING ONLINE ADVERTISEMENTS USING INFORMATION RETRIEVAL

169 Pages·2006·0.81 MB·English

by  Academic Technology

SCHEDULING ONLINE ADVERTISEMENTS USING INFORMATION RETRIEVAL
AND NEURAL NETWORK/GENETIC ALGORITHM BASED METAHEURISTICS

By

JASON DEANE

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2006
Copyright 2006

by

Jason Deane
This document is dedicated to my entire family, but especially to Amanda, Conner, mom
and pawpaw. Amanda and Conner provided me with the necessary strength,
determination and never ending support and were my inspiration in pursuing and
finishing my PhD. Mom and pawpaw are, without a doubt, the two most influential
people in my life. For good and for bad, everything that I am is as a result of my never
ending effort to model myself after these two amazing people. Pawpaw was the kindest
and most sincere person that I have ever met and although he’s in a better place now, I
still think of him every day. My mother is the strongest and hardest working person that I
know and without her many sacrifices, my life could have been completely different and
I would never have had the opportunity to achieve this goal. Thank you!
ACKNOWLEDGMENTS
I would like to especially thank my wife Amanda for supporting and putting up
with me throughout this process. I know that it was not easy. I would also like to thank
our families for their never ending support throughout this very challenging endeavor. In
addition, I would like to thank my dissertation committee and the DIS department staff
for their support and guidance. In particular I would like to thank and acknowledge my
advisor, Anurag Agarwal, and my co-chair, Praveen Pathak, for their countless hours of
training and support. I couldn’t have done it without you!!

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TABLE OF CONTENTS

page
ACKNOWLEDGMENTS.................................................................................................iv
LIST OF TABLES............................................................................................................vii
LIST OF FIGURES...........................................................................................................ix
ABSTRACT.........................................................................................................................x
CHAPTER
1 INTRODUCTION AND MOTIVATION....................................................................1
2 ONLINE ADVERTISING............................................................................................5
2.1 Definitions and Pricing Models..............................................................................5
2.2 Literature Review..................................................................................................7
3 INFORMATION RETRIEVAL METHODOLOGIES..............................................27
3.1 Overview...............................................................................................................27
3.2 Data Pre-processing..............................................................................................30
3.3 Vector Space Model.............................................................................................31
3.4 Structural Representation.....................................................................................34
3.5 WordNet...............................................................................................................35
4 LARGE SCALE SEARCH METHODOLOGIES.....................................................38
4.1 Overview...............................................................................................................38
4.2 Genetic Algorithms...............................................................................................43
4.3 Neural Networks...................................................................................................47
4.4 The No Free Lunch Theorem...............................................................................52
5 RESEARCH MODEL(S)...........................................................................................54
5.1 Problem Summary................................................................................................54
5.2 Information Retrieval Based Ad Targeting..........................................................56
5.3 Online Advertisement Scheduling.......................................................................64
5.3.1 The Modified Maxspace Problem (MMS).................................................65
5.3.2 The Modified Maxspace Problem with Ad Targeting (MMSwAT)..........68
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5.3.3 The Modified Maxspace Problem with Non-Linear Pricing
(MMSwNLP)...................................................................................................72
5.4 Model Solution Approaches.................................................................................74
5.4.1 Augmented Neural Network (AugNN)......................................................74
5.4.2 Genetic Algorithm (GA).............................................................................78
5.4.3 Hybrid Technique.......................................................................................82
5.4.4 Parameter Selection....................................................................................83
5.5 Problem set Development.....................................................................................84
6 RESULTS...................................................................................................................86
6.1 Information Retrieval Based Ad Targeting Results..............................................86
6.2 Discussion of the Information Retrieval Based Ad Targeting Results...............101
6.3 Online Advertisement Scheduling Results.........................................................102
6.2.1 Modified Maxspace (MMS) Problem Result...........................................104
6.2.2 The Modified Maxspace with Ad Targeting (MMSwAT) Problem
Results............................................................................................................105
6.2.3 The Modified Maxspace wint Nonlinear Pricing (MMSwNLP) Problem
Results............................................................................................................107
6.3 Discussion of the Online Advertisement Scheduling Results............................109
7 SUMMARY, CONCLUSIONS AND FUTURE RESEARCH................................110
APPENDIX
A GA AND AUGNN PARAMETER AND SETTING DEFINITIONS.....................113
B LIST OF ADVERTISED PRODUCTS AND SERVICES AND THEIR
RESPECTIVE CHARACTERISTIC ARRAYS......................................................114
C SAMPLE DOCUMENTS FOR ONE USER FROM THE IR BASED AD
TARGETING PROCESS.........................................................................................119
LIST OF REFERENCES.................................................................................................149
BIOGRAPHICAL SKETCH...........................................................................................159

vi
LIST OF TABLES
Table page

1 Structural Element Weighting Schemes...................................................................60
2 AugNN Parameter Values........................................................................................84
3 GA Parameter Values...............................................................................................84
4 Hybrid Parameter Values.........................................................................................84
5 Summary of Mean Student Rankings for the 4 Selection Methods.........................88
6 T Test-Scheme 1 & Random Selection....................................................................89
7 T Test-Scheme 2 & Random Selection....................................................................89
8 T Test-Scheme 3 & Random Selection....................................................................90
9 Summary of Mean Student Rankings for the Three Weighting Schemes................91
10 T Test-Scheme 1-5 & Scheme 1-1...........................................................................92
11 T Test-Scheme 1-5 & Scheme 1-2...........................................................................92
12 T Test-Scheme 1-5 & Scheme 1-3...........................................................................93
13 T Test-Scheme 1-5 & Scheme 1-4...........................................................................93
14 T Test-Scheme 2-5 & Scheme 2-1...........................................................................94
15 T Test-Scheme 2-5 & Scheme 2-2...........................................................................94
16 T Test-Scheme 2-5 & Scheme 2-3...........................................................................95
17 T Test-Scheme 2-5 & Scheme 2-4...........................................................................95
18 T Test-Scheme 3-5 & Scheme 3-1...........................................................................96
19 T Test-Scheme 3-5 & Scheme 3-2...........................................................................96
20 T Test-Scheme 3-5 & Scheme 3-3...........................................................................97
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21 T Test-Scheme 3-5 & Scheme 3-4...........................................................................97
22 T Test-Scheme 1 & Scheme 2.................................................................................99
23 T Test-Scheme 1 & Scheme 3.................................................................................99
24 T Test-Scheme 2 & Scheme 3................................................................................100
25 Problem Results......................................................................................................104
26 MMSwAT Comparison of Results.........................................................................105
27 MMSwNLP Comparison of Results......................................................................107

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LIST OF FIGURES
Figure page

1 A Screen Print of Yahoo’s Shopping Page. Notice the advertising banner down
the right hand side of the Web page.........................................................................10
2 Pictorial Representation of Information Flow in Traditional Print Advertising......18
3 Pictorial Representation of the Information Flow in Online Advertising................19
4 Geometric Representation of the VSM....................................................................32
5 Classes of Search Methods (Basic Model Borrowed from [54]).............................40
6 Pictorial Representation of the Cerebral Cortex [91]..............................................48
7 Pictorial Representation of a Basic Feed Forward ANN [91]..................................50
8 Selected Parents Prior to Crossover.........................................................................80
9 Resulting Offspring..................................................................................................81
10 Child 2 Prior to Mutation.........................................................................................81
11 Child 2 After Mutation.............................................................................................81
12 Q-Q Plot of Student Response Values.....................................................................87

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Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
SCHEDULING ONLINE ADVERTISEMENTS USING INFORMATION RETRIEVAL
AND NEURAL NETWORK/GENETIC ALGORITHM BASED METAHEURISTICS

By
Jason Deane

August, 2006
Chair: Anurag Agarwal
Cochair: Praveen Pathak
Major Department: Decision and Information Sciences
As a result of the recent technological proliferation, online advertising has become
a very powerful and popular method of marketing; industry revenue is growing at a
record pace. One very challenging problem which is faced by those on the publishing
side of the industry is ad targeting. In an attempt to maximize revenue, publishers try
their best to expose web surfers to a set of advertisements which are closely aligned with
their interests and needs. In this work, we present and test an information retrieval based
ad targeting technique which shows promise as an alternative solution method for this
problem. A second, very difficult, challenge faced by online ad publishers is the
development of an ad schedule which makes the most efficient use of their available
advertisement space. We introduce three versions of this very difficult problem and test
several potential solution techniques for each of them.

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