Dr. Liu Peng is senior director and chief architect of business products at Qihoo 360. He is

also responsible for product and engineering for monetization of 360. After receiving his

PhD from Tsinghua University in 2005, he joined Microsoft Research Asia and studied

cutting-edge artificial intelligence technologies. In 2009, he participated in the founding of

Yahoo! Labs Beijing as a senior scientist. He was also chief scientist of MediaV. Dr. Liu

Peng is devoted to products and technologies related to big data and computational

advertising. His public online course "computational advertising" has attracted more than

30,000 students on [...], and has been adopted as a basic training material in

many related companies. Moreover, this course has been selected by Peking University,

Tsinghua University and Beihang University for their graduates.

Wang Chao received his master's degree from Peking University, and then worked at

Weibo and Autohome's advertising department for some years. He is now a tech leader in

the query recommendation group at Baidu's portal search department. His work focuses on

machine learning algorithms in computational advertising, and he has won 7th place among

718 participants in "predict click-through rates on display ads" organized by Kaggle and

Criteo. He is also interested in contributing code for open source machine learning tools

such as xgboost.

Contents

Figures, xxi

Tables, xxvii

Foreword, xxix

Preface (1), xxxvii

Preface (2), xxxix

Preface (3), xli

Authors, xliii

PART 1 Market and Background of Online Advertising 1

CHAPTER 1 ¿ Overview of Online Advertising 3

1.1 FREE MODE AND CORE ASSETS OF THE INTERNET 4

1.2 RELATIONSHIP BETWEEN BIG DATA AND ADVERTISING 5

1.3 DEFINITION AND PURPOSE OF ADVERTISING 8

1.4 PRESENTATION FORMS OF ONLINE ADVERTISING 10

1.5 BRIEF HISTORY OF ONLINE ADVERTISING 18

CHAPTER 2 ¿ Basis for Computational Advertising 25

2.1 ADVERTISING EFFECTIVENESS THEORY 26

2.2 TECHNICAL FEATURES OF THE INTERNET ADVERTISING 29

2.3 CORE ISSUE OF COMPUTATIONAL ADVERTISING 30

2.3.1 Breakdown of Advertising Return 32

2.3.2 Relationship between Billing Models and eCPM Estimation 33

2.4 BUSINESS ORGANIZATIONS IN THE ONLINE ADVERTISING

INDUSTRY 36

2.4.1 Interactive Advertising Bureau 37

2.4.2 American Association of Advertising Agencies 38

2.4.3 Association of National Advertisers 38

PART 2 Product Logic of Online Advertising 39

CHAPTER 3 ¿ Overview of Online Advertising Products 41

3.1 DESIGN PHILOSOPHY FOR COMMERCIAL PRODUCTS 43

3.2 PRODUCT INTERFACE OF ADVERTISING SYSTEM 44

3.2.1 Demand-Side Management Interface 44

3.2.2 Supply-Side Management Interface 47

3.2.3 Multiple Forms of Interface between Supply and Demand Sides 48

CHAPTER 4 ¿ Agreement-Based Advertising 51

4.1 AD SPACE AGREEMENT 52

4.2 AUDIENCE TARGETING 53

4.2.1 Overview of Audience Targeting Technologies 54

4.2.2 Audience Targeting Tag System 57

4.2.3 Design Principles for Tag System 59

4.3 DISPLAY QUANTITY AGREEMENT 60

4.3.1 Traffic Forecasting 61

4.3.2 Traffic Shaping 61

4.3.3 Online Allocation 62

4.3.4 Product Cases 63

4.3.4.1 Yahoo! GD 63

CHAPTER 5 ¿ Search Ad and Auction-Based Advertising 65

5.1 SEARCH AD 67

5.1.1 Products of Search Advertising 67

5.1.2 New Forms of Search Ads 70

5.1.3 Product Strategy of Search Advertising 73

5.1.4 Product Cases 76

5.2 POSITION AUCTION AND MECHANISM DESIGN 79

5.2.1 Market Reserve Price 80

5.2.2 Pricing Problem 81

5.2.3 Squashing 83

5.2.4 Myerson Optimal Auction 84

5.2.5 Examples of Pricing Results 85

5.3 AUCTION-BASED ADN 85

5.3.1 Forms of ADN Products 86

5.3.2 Product Strategy for ADN 88

5.3.3 Product Cases 89

5.4 DEMAND-SIDE PRODUCTS IN AUCTION-BASED ADVERTISING 90

5.4.1 Search Engine Marketing 90

5.4.2 Trading Desk 91

5.4.3 Product Cases 91

5.5 COMPARISON BETWEEN AUCTION-BASED AND

AGREEMENT-BASED ADVERTISING 93

CHAPTER 6 ¿ Programmatic Trade Advertising 95

6.1 RTB 97

6.1.1 RTB Process 98

6.2 OTHER MODES OF PROGRAMMED TRADE 100

6.2.1 Preferred Deal 100

6.2.2 Private Marketplace 101

6.2.3 Programmatic Direct Buy 102

6.2.4 Spectrum of Advertising Transactions 103

6.3 AD EXCHANGE 104

6.3.1 Product Samples 104

6.4 DEMAND-SIDE PLATFORM 105

6.4.1 DSP Product Strategy 106

6.4.2 Bidding Strategy 106

6.4.3 Bidding and Pricing Processes 108

6.4.4 Retargeting 108

6.4.5 Look-Alike 111

6.4.6 Product Cases 112

6.5 SUPPLY-SIDE PLATFORM 113

6.5.1 SSP Product Strategy 114

6.5.2 Header Bidding 115

6.5.3 Product Cases 117

CHAPTER 7 ¿ Data Processing and Exchange 119

7.1 VALUABLE DATA SOURCES 120

7.2 DATA MANAGEMENT PLATFORM 123

7.2.1 Tripartite Data Partitioning 123

7.2.2 First-Party DMP 123

7.2.3 Third-Party DMP 124

7.2.4 Product Cases 125

7.3 BASIC PROCESS OF DATA TRADING 129

7.4 PRIVACY PROTECTION AND DATA SECURITY 131

7.4.1 Privacy Protection 131

7.4.2 Data Security in Programmatic Trade 134

7.4.3 General Data Protection Regulations 136

CHAPTER 8 ¿ News Feed Ad and Native Ad 139

8.1 STATUS QUO AND CHALLENGES IN MOBILE ADVERTISING 140

8.1.1 Characteristics of Mobile Advertising 141

8.1.2 Traditional Creative of Mobile Advertising 142

8.1.3 Challenges in Front of Mobile Advertising 144

8.2 NEWS FEED AD 146

8.2.1 Definition of News Feed Ad 146

8.2.2 Key Points about News Feed Ad 149

8.3 OTHER NATIVE AD-RELATED PRODUCTS 150

8.3.1 Search Ad 150

8.3.2 Advertorial 151

8.3.3 Affiliate network 151

8.4 NATIVE ADVERTISING PLATFORM 151

8.4.1 Native Display and Native Scenario 152

8.4.2 Scenario Perception and Application 153

8.4.3 Product Placement Native Ad 154

8.4.4 Product Cases 157

8.5 NATIVE AD AND PROGRAMMATIC TRADE 161

PART 3 Key Technologies for Computational Advertising 163

CHAPTER 9 ¿ Technological Overview 165

9.1 PERSONALIZED SYSTEM FRAMEWORK 166

9.2 OPTIMIZATION GOALS OF VARIOUS ADVERTISING SYSTEMS 167

9.3 COMPUTATIONAL ADVERTISING SYSTEM ARCHITECTURE 169

9.3.1 Ad Serving Engine 169

9.3.2 Data Highway 172

9.3.3 Offline Data Processing 172

9.3.4 Online Data Processing 173

9.4 MAIN TECHNOLOGIES FOR COMPUTATIONAL

ADVERTISING SYSTEM 174

9.5 BUILD A COMPUTATIONAL ADVERTISING SYSTEM WITH

OPEN SOURCE TOOLS 175

9.5.1 Web Server Nginx 176

9.5.2 ZooKeeper: Distributed Configuration and Cluster

Management Tool 178

9.5.3 Lucene: Full-Text Retrieval Engine 179

9.5.4 Thrift: Cross-Language Communication Interface 179

9.5.5 Data Highway 180

9.5.6 Hadoop: Distributed Data-Processing Platform 181

9.5.7 Redis: Online Cache of Features 182

9.5.8 Strom: Stream Computing Platform Storm 182

9.5.9 Spark: Efficient Iterative Computing Framework 183

CHAPTER 10 ¿ Fundamental Knowledge 185

10.1 INFORMATION RETRIEVAL 186

10.1.1 Inverted Index 186

10.1.2 Vector Space Model 189

10.2 OPTIMIZATION 190

10.2.1 Lagrange Multiplier and Convex Optimization 191

10.2.2 Downhill Simplex Method 192

10.2.3 Gradient Descent 193

10.2.4 Quasi-Newton Methods 195

10.2.5 Trust Region Method 199

10.3 STATISTICAL MACHINE LEARNING 201

10.3.1 Maximum Entropy and Exponential Family Distribution 202

10.3.2 Mixture Model and EM Algorithm 204

10.3.3 Bayesian Learning 206

10.4 DISTRIBUTED OPTIMIZATION FRAMEWORK FOR

STATISTICAL MODEL 210

10.5 DEEP LEARNING 211

10.5.1 DNN Optimization Methods 212

10.5.2 Convolutional Neural Network 214

10.5.3 Recursive Neural Network 215

10.5.4 Generative Adversarial Nets 217

CHAPTER 11 ¿ Agreement-Based Advertising Technologies 219

11.1 ADVERTISING SCHEDULING SYSTEM 220

11.1.1 Scheduling and Mixed Ad Serving 220

11.2 GD SYSTEM 221

11.2.1 Traffic Forecasting 222

11.2.2 Frequency Capping 224

11.3 ONLINE ALLOCATION 227

11.3.1 Online Allocation Problem 228

11.3.2 Examples of Online Allocation Problems 230

11.3.3 Limit Performance Analysis 232

11.3.4 Practical Optimization Algorithms 233

11.4 HEURISTIC ALLOCATION PLAN HWM 240

CHAPTER 12 ¿ Audience-Targeting Technologies 245

12.1 CLASSIFICATION OF AUDIENCE TARGETING TECHNOLOGIES 246

12.2 CONTEXTUAL TARGETING 248

12.2.1 Near-Line Crawling System 249

12.3 TEXT TOPIC MINING 250

12.3.1 LSA Model 250

12.3.2 PLSI Model 251

12.3.3 LDA Model 252

12.3.4 Word Embedding (Word2vec) 253

12.4 BEHAVIORAL TARGETING 255

12.4.1 Modeling Problem for Behavioral Targeting 255

12.4.2 Feature Generation for Behavioral Targeting 257

12.4.2.1 Tagging Methods for Various Behaviors 260

12.4.3 Decision-making Process for Behavioral Targeting 261

12.4.4 Evaluation of Behavioral Targeting 262

12.5 PREDICTION OF DEMOGRAPHICAL ATTRIBUTES 264

12.6 DATA MANAGEMENT PLATFORM 266

CHAPTER 13 ¿ Auction-Based Advertising Technologies 267

13.1 PRICING ALGORITHMS IN AUCTION-BASED ADVERTISING 268

13.2 SEARCH AD SYSTEM 270

13.2.1 Query Expansion 272

13.2.2 Ad Placement 274

13.3 ADN 275

13.3.1 Short-Term Behavior Feedback and Stream Computing 275

13.4 AD RETRIEVAL 278

13.4.1 Boolean Expression 279

13.4.2 Relevance Retrieval 283

13.4.3 DNN-Based Semantic Modeling 288

13.4.4 ANN Semantic Retrieval 292

CHAPTER 14 ¿ CTR Prediction Model 301

14.1 CTR PREDICTION 302

14.1.1 CTR Basic Model 302

14.1.2 LR Model-Based Optimization Algorithm 303

14.1.3 Correction of CTR Model 312

14.1.4 Features of CTR Model 313

14.1.5 Evaluation of CTR Model 319

14.1.6 Intelligent Frequency Capping 321

14.2 OTHER CTR MODELS 322

14.2.1 Factorization Machines 322

14.2.2 GBDT 323

14.2.3 Deep Learning-Based CTR Model 324

14.3 EXPLORATION AND UTILIZATION 326

14.3.1 Reinforcement Learning and E&E 327

14.3.2 UCB 329

14.3.3 Contextual Bandit 329

CHAPTER 15 ¿ Programmatic Trade Technologies 331

15.1 ADX 332

15.1.1 Cookie Mapping 334

15.1.2 Call-out Optimization 336

15.2 DSP 338

15.2.1 Customized User Segmentation 340

15.2.1.1 Look-Alike Modeling 341

15.2.2 CTR Prediction in DSP 342

15.2.3 Estimation of Click Value 343

15.2.4 Bidding Strategy 344

15.3 SSP 345

15.3.1 Network Optimization 346

CHAPTER 16 ¿ Other Advertising Technologies 347

16.1 CREATIVE OPTIMIZATION 348

16.1.1 Programmatic Creative 349

16.1.2 Click Heat Map 350

16.1.3 Trend of Creative 351

16.2 EXPERIMENTAL FRAMEWORK 353

16.3 ADVERTISING MONITORING AND ATTRIBUTION 354

16.3.1 Ad Monitoring 355

16.3.2 Ad Safety...