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Computational Advertising: Market and Technologies for Internet Commercial Monetization 2nd edition [Kõva köide]

(University of Science and Technology of China, Anhui), (University of Manitoba, Canada)
  • Formaat: Hardback, 442 pages, kõrgus x laius: 254x178 mm, kaal: 957 g, 17 Tables, black and white; 131 Illustrations, black and white
  • Ilmumisaeg: 27-May-2020
  • Kirjastus: CRC Press
  • ISBN-10: 0367206382
  • ISBN-13: 9780367206383
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Computational Advertising: Market and Technologies for Internet Commercial Monetization 2nd editionSuurem pilt
  • Formaat: Hardback, 442 pages, kõrgus x laius: 254x178 mm, kaal: 957 g, 17 Tables, black and white; 131 Illustrations, black and white
  • Ilmumisaeg: 27-May-2020
  • Kirjastus: CRC Press
  • ISBN-10: 0367206382
  • ISBN-13: 9780367206383
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Märksõnad:

This book introduces computational advertising, and Internet monetization. It provides a macroscopic understanding of how consumer products in the Internet era push user experience and monetization to the limit. Part One of the book focuses on the basic problems and background knowledge of online advertising. Part Two targets the product, operations, and sales staff, as well as high-level decision makers of the Internet products. It explains the market structure, trading models, and the main products in computational advertising. Part Three targets systems, algorithms, and architects, and focuses on the key technical challenges of different advertising products.

 

Features

·         Introduces computational advertising and Internet monetization

·         Covers data processing, utilization, and trading

·         Uses business logic as the driving force to explain online advertising products and technology advancement

·         Explores the products and the technologies of computational advertising, to provide insights on the realization of personalization systems, constrained optimization, data monetization and trading, and other practical industry problems

·         Includes case studies and code snippets

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(38)
Chapter 1 Overview of Online Advertising
3(22)
1.1 Free Mode and Core Assets of the Internet
4(1)
1.2 Relationship Between Big Data and Advertising
5(3)
1.3 Definition and Purpose of Advertising
8(2)
1.4 Presentation Forms of Online Advertising
10(8)
1.5 Brief History of Online Advertising
18(7)
Chapter 2 Basis for Computational Advertising
25(14)
2.1 Advertising Effectiveness Theory
26(3)
2.2 Technical Features of the Internet Advertising
29(1)
2.3 Core Issue of Computational Advertising
30(6)
2.3.1 Breakdown of Advertising Return
32(1)
2.3.2 Relationship between Billing Models and eCPM Estimation
33(3)
2.4 Business Organizations in the Online Advertising Industry
36(3)
2.4.1 Interactive Advertising Bureau
37(1)
2.4.2 American Association of Advertising Agencies
38(1)
2.4.3 Association of National Advertisers
38(1)
Part 2 Product Logic of Online Advertising
39(124)
Chapter 3 Overview of Online Advertising Products
41(10)
3.1 Design Philosophy for Commercial Products
43(1)
3.2 Product Interface of Advertising System
44(7)
3.2.1 Demand-Side Management Interface
44(3)
3.2.2 Supply-Side Management Interface
47(1)
3.2.3 Multiple Forms of Interface between Supply and Demand Sides
48(3)
Chapter 4 Agreement-Based Advertising
51(14)
4.1 Ad Space Agreement
52(1)
4.2 Audience Targeting
53(7)
4.2.1 Overview of Audience Targeting Technologies
54(3)
4.2.2 Audience Targeting Tag System
57(2)
4.2.3 Design Principles for Tag System
59(1)
4.3 Display Quantity Agreement
60(5)
4.3.1 Traffic Forecasting
61(1)
4.3.2 Traffic Shaping
61(1)
4.3.3 Online Allocation
62(1)
4.3.4 Product Cases
63(1)
4.3.4.1 Yahoo! GD
63(2)
Chapter 5 Search Ad and Auction-Based Advertising
65(30)
5.1 Search Ad
67(12)
5.1.1 Products of Search Advertising
67(3)
5.1.2 New Forms of Search Ads
70(3)
5.1.3 Product Strategy of Search Advertising
73(3)
5.1.4 Product Cases
76(3)
5.2 Position Auction and Mechanism Design
79(6)
5.2.1 Market Reserve Price
80(1)
5.2.2 Pricing Problem
81(2)
5.2.3 Squashing
83(1)
5.2.4 Myerson Optimal Auction
84(1)
5.2.5 Examples of Pricing Results
85(1)
5.3 Auction-Based Adn
85(5)
5.3.1 Forms of ADN Products
86(2)
5.3.2 Product Strategy for ADN
88(1)
5.3.3 Product Cases
89(1)
5.4 Demand-Side Products in Auction-Based Advertising
90(3)
5.4.1 Search Engine Marketing
90(1)
5.4.2 Trading Desk
91(1)
5.4.3 Product Cases
91(2)
5.5 Comparison Between Auction-Based and Agreement-Based Advertising
93(2)
Chapter 6 Programmatic Trade Advertising
95(24)
6.1 Rtb
97(3)
6.1.1 RTB Process
98(2)
6.2 Other Modes of Programmed Trade
100(4)
6.2.1 Preferred Deal
100(1)
6.2.2 Private Marketplace
101(1)
6.2.3 Programmatic Direct Buy
102(1)
6.2.4 Spectrum of Advertising Transactions
103(1)
6.3 Ad Exchange
104(1)
6.3.1 Product Samples
104(1)
6.4 Demand-Side Platform
105(8)
6.4.1 DSP Product Strategy
106(1)
6.4.2 Bidding Strategy
106(2)
6.4.3 Bidding and Pricing Processes
108(1)
6.4.4 Retargeting
108(3)
6.4.5 Look-Alike
111(1)
6.4.6 Product Cases
112(1)
6.5 Supply-Side Platform
113(6)
6.5.1 SSP Product Strategy
114(1)
6.5.2 Header Bidding
115(2)
6.5.3 Product Cases
117(2)
Chapter 7 Data Processing and Exchange
119(20)
7.1 Valuable Data Sources
120(3)
7.2 Data Management Platform
123(6)
7.2.1 Tripartite Data Partitioning
123(1)
7.2.2 First-Party DMP
123(1)
7.2.3 Third-Party DMP
124(1)
7.2.4 Product Cases
125(4)
7.3 Basic Process of Data Trading
129(2)
7.4 Privacy Protection and Data Security
131(8)
7.4.1 Privacy Protection
131(3)
7.4.2 Data Security in Programmatic Trade
134(2)
7.4.3 General Data Protection Regulations
136(3)
Chapter 8 News Feed Ad and Native Ad
139(24)
8.1 Status Quo and Challenges in Mobile Advertising
140(6)
8.1.1 Characteristics of Mobile Advertising
141(1)
8.1.2 Traditional Creative of Mobile Advertising
142(2)
8.1.3 Challenges in Front of Mobile Advertising
144(2)
8.2 News Feed Ad
146(4)
8.2.1 Definition of News Feed Ad
146(3)
8.2.2 Key Points about News Feed Ad
149(1)
8.3 Other Native Ad-Related Products
150(1)
8.3.1 Search Ad
150(1)
8.3.2 Advertorial
151(1)
8.3.3 Affiliate network
151(1)
8.4 Native Advertising Platform
151(10)
8.4.1 Native Display and Native Scenario
152(1)
8.4.2 Scenario Perception and Application
153(1)
8.4.3 Product Placement Native Ad
154(3)
8.4.4 Product Cases
157(4)
8.5 Native Ad and Programmatic Trade
161(2)
Part 3 Key Technologies for Computational Advertising
163(212)
Chapter 9 Technological Overview
165(20)
9.1 Personalized System Framework
166(1)
9.2 Optimization Goals of Various Advertising Systems
167(2)
9.3 Computational Advertising System Architecture
169(5)
9.3.1 Ad Serving Engine
169(3)
9.3.2 Data Highway
172(1)
9.3.3 Offline Data Processing
172(1)
9.3.4 Online Data Processing
173(1)
9.4 Main Technologies For Computational Advertising System
174(1)
9.5 Build A Computational Advertising System With Open Source Tools
175(10)
9.5.1 Web Server Nginx
176(2)
9.5.2 ZooKeeper: Distributed Configuration and Cluster Management Tool
178(1)
9.5.3 Lucene: Full-Text Retrieval Engine
179(1)
9.5.4 Thrift: Cross-Language Communication Interface
179(1)
9.5.5 Data Highway
180(1)
9.5.6 Hadoop: Distributed Data-Processing Platform
181(1)
9.5.7 Redis: Online Cache of Features
182(1)
9.5.8 Strom: Stream Computing Platform Storm
182(1)
9.5.9 Spark: Efficient Iterative Computing Framework
183(2)
Chapter 10 Fundamental Knowledge
185(34)
10.1 Information Retrieval
186(4)
10.1.1 Inverted Index
186(3)
10.1.2 Vector Space Model
189(1)
10.2 Optimization
190(11)
10.2.1 Lagrange Multiplier and Convex Optimization
191(1)
10.2.2 Downhill Simplex Method
192(1)
10.2.3 Gradient Descent
193(2)
10.2.4 Quasi-Newton Methods
195(4)
10.2.5 Trust Region Method
199(2)
10.3 Statistical Machine Learning
201(9)
10.3.1 Maximum Entropy and Exponential Family Distribution
202(2)
10.3.2 Mixture Model and EM Algorithm
204(2)
10.3.3 Bayesian Learning
206(4)
10.4 Distributed Optimization Framework For Statistical Model
210(1)
10.5 Deep Learning
211(8)
10.5.1 DNN Optimization Methods
212(2)
10.5.2 Convolutional Neural Network
214(1)
10.5.3 Recursive Neural Network
215(2)
10.5.4 Generative Adversarial Nets
217(2)
Chapter 11 Agreement-Based Advertising Technologies
219(26)
11.1 Advertising Scheduling System
220(1)
11.1.1 Scheduling and Mixed Ad Serving
220(1)
11.2 Gd System
221(6)
11.2.1 Traffic Forecasting
222(2)
11.2.2 Frequency Capping
224(3)
11.3 Online Allocation
227(13)
11.3.1 Online Allocation Problem
228(2)
11.3.2 Examples of Online Allocation Problems
230(2)
11.3.3 Limit Performance Analysis
232(1)
11.3.4 Practical Optimization Algorithms
233(7)
11.4 Heuristic Allocation Plan Hwm
240(5)
Chapter 12 Audience-Targeting Technologies
245(22)
12.1 Classification of Audience Targeting Technologies
246(2)
12.2 Contextual Targeting
248(2)
12.2.1 Near-Line Crawling System
249(1)
12.3 Text Topic Mining
250(5)
12.3.1 LSA Model
250(1)
12.3.2 PLSI Model
251(1)
12.3.3 LDA Model
252(1)
12.3.4 Word Embedding (Word2vec)
253(2)
12.4 Behavioral Targeting
255(9)
12.4.1 Modeling Problem for Behavioral Targeting
255(2)
12.4.2 Feature Generation for Behavioral Targeting
257(3)
12.4.2.1 Tagging Methods for Various Behaviors
260(1)
12.4.3 Decision-making Process for Behavioral Targeting
261(1)
12.4.4 Evaluation of Behavioral Targeting
262(2)
12.5 Prediction of Demographical Attributes
264(2)
12.6 Data Management Platform
266(1)
Chapter 13 Auction-Based Advertising Technologies
267(34)
13.1 Pricing Algorithms in Auction-Based Advertising
268(2)
13.2 Search Ad System
270(5)
13.2.1 Query Expansion
272(2)
13.2.2 Ad Placement
274(1)
13.3 Adn
275(3)
13.3.1 Short-Term Behavior Feedback and Stream Computing
275(3)
13.4 Ad Retrieval
278(23)
13.4.1 Boolean Expression
279(4)
13.4.2 Relevance Retrieval
283(5)
13.4.3 DNN-Based Semantic Modeling
288(4)
13.4.4 ANN Semantic Retrieval
292(9)
Chapter 14 CTR Prediction Model
301(30)
14.1 Ctr Prediction
302(20)
14.1.1 CTR Basic Model
302(1)
14.1.2 LR Model-Based Optimization Algorithm
303(9)
14.1.3 Correction of CTR Model
312(1)
14.1.4 Features of CTR Model
313(6)
14.1.5 Evaluation of CTR Model
319(2)
14.1.6 Intelligent Frequency Capping
321(1)
14.2 Other Ctr Models
322(4)
14.2.1 Factorization Machines
322(1)
14.2.2 GBDT
323(1)
14.2.3 Deep Learning-Based CTR Model
324(2)
14.3 Exploration and Utilization
326(5)
14.3.1 Reinforcement Learning and E&E
327(2)
14.3.2 UCB
329(1)
14.3.3 Contextual Bandit
329(2)
Chapter 15 Programmatic Trade Technologies
331(16)
15.1 Adx
332(6)
15.1.1 Cookie Mapping
334(2)
15.1.2 Call-out Optimization
336(2)
15.2 Dsp
338(7)
15.2.1 Customized User Segmentation
340(1)
15.2.1.1 Look-Alike Modeling
341(1)
15.2.2 CTR Prediction in DSP
342(1)
15.2.3 Estimation of Click Value
343(1)
15.2.4 Bidding Strategy
344(1)
15.3 Ssp
345(2)
15.3.1 Network Optimization
346(1)
Chapter 16 Other Advertising Technologies
347(28)
16.1 Creative Optimization
348(5)
16.1.1 Programmatic Creative
349(1)
16.1.2 Click Heat Map
350(1)
16.1.3 Trend of Creative
351(2)
16.2 Experimental Framework
353(1)
16.3 Advertising Monitoring and Attribution
354(5)
16.3.1 Ad Monitoring
355(1)
16.3.2 Ad Safety
356(1)
16.3.3 Attribution of Advertising Performance
357(2)
16.4 Spam and Anti-Spam
359(7)
16.4.1 Classification of Spam Methods
359(1)
16.4.2 Common Ad Spam Methods
360(6)
16.5 Product and Technology Selection
366(9)
16.5.1 Best Practices for Media
367(3)
16.5.2 Best Practices for Advertisers
370(2)
16.5.3 Best Practices for Data Providers
372(3)
Part 4 Terminology and Index
375(6)
References 381(6)
Index 387
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 Netease.com, 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 masters degree from Peking University, and then worked at

Weibo and Autohomes advertising department for some years. He is now a tech leader in

the query recommendation group at Baidus 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.