Computational Advertising Market and Technologies for Internet Commercial Monetization 2nd Edition by Peng Liu, Chao Wang ISBN 9780429557729 0429557728

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ISBN 10: 0429557728
ISBN 13: 9780429557729
Author: Peng Liu, Chao Wang

Computational Advertising Market and Technologies for Internet Commercial Monetization 2nd Edition Table of contents:

PART 1: Market and Background of Online Advertising

CHAPTER 1. Overview of Online Advertising

1.1 Free Mode and Core Assets of the Internet

1.2 Relationship between Big Data and Advertising

1.3 Definition and Purpose of Advertising

1.4 Presentation Forms of Online Advertising

1.5 Brief History of Online Advertising

CHAPTER 2. Basis for Computational Advertising

2.1 Advertising Effectiveness Theory

2.2 Technical Features of the Internet Advertising

2.3 Core Issue of Computational Advertising

2.3.1 Breakdown of Advertising Return

2.3.2 Relationship between Billing Models and eCPM Estimation

2.4 Business Organizations in the Online Advertising Industry

2.4.1 Interactive Advertising Bureau

2.4.2 American Association of Advertising Agencies

2.4.3 Association of National Advertisers

PART 2: Product Logic of Online Advertising

CHAPTER 3. Overview of Online Advertising Products

3.1 Design Philosophy for Commercial Products

3.2 Product Interface of Advertising System

3.2.1 Demand-Side Management Interface

3.2.2 Supply-Side Management Interface

3.2.3 Multiple Forms of Interface between Supply and Demand Sides

CHAPTER 4. Agreement-Based Advertising

4.1 Ad Space Agreement

4.2 Audience Targeting

4.2.1 Overview of Audience Targeting Technologies

4.2.2 Audience Targeting Tag System

4.2.3 Design Principles for Tag System

4.3 Display Quantity Agreement

4.3.1 Traffic Forecasting

4.3.2 Traffic Shaping

4.3.3 Online Allocation

4.3.4 Product Cases

4.3.4.1 Yahoo! GD

CHAPTER 5. Search Ad and Auction-Based Advertising

5.1 Search Ad

5.1.1 Products of Search Advertising

5.1.2 New Forms of Search Ads

5.1.3 Product Strategy of Search Advertising

5.1.4 Product Cases

5.2 Position Auction and Mechanism Design

5.2.1 Market Reserve Price

5.2.2 Pricing Problem

5.2.3 Squashing

5.2.4 Myerson Optimal Auction

5.2.5 Examples of Pricing Results

5.3 Auction-Based ADN

5.3.1 Forms of ADN Products

5.3.2 Product Strategy for ADN

5.3.3 Product Cases

5.4 Demand-Side Products in Auction-Based Advertising

5.4.1 Search Engine Marketing

5.4.2 Trading Desk

5.4.3 Product Cases

5.5 Comparison between Auction-Based and Agreement-Based Advertising

CHAPTER 6. Programmatic Trade Advertising

6.1 RTB

6.1.1 RTB Process

6.2 Other Modes of Programmed Trade

6.2.1 Preferred Deal

6.2.2 Private Marketplace

6.2.3 Programmatic Direct Buy

6.2.4 Spectrum of Advertising Transactions

6.3 Ad Exchange

6.3.1 Product Samples

6.4 Demand-Side Platform

6.4.1 DSP Product Strategy

6.4.2 Bidding Strategy

6.4.3 Bidding and Pricing Processes

6.4.4 Retargeting

6.4.5 Look-Alike

6.4.6 Product Cases

6.5 Supply-Side Platform

6.5.1 SSP Product Strategy

6.5.2 Header Bidding

6.5.3 Product Cases

CHAPTER 7. Data Processing and Exchange

7.1 Valuable Data Sources

7.2 Data Management Platform

7.2.1 Tripartite Data Partitioning

7.2.2 First-Party DMP

7.2.3 Third-Party DMP

7.2.4 Product Cases

7.3 Basic Process of Data Trading

7.4 Privacy Protection and Data Security

7.4.1 Privacy Protection

7.4.2 Data Security in Programmatic Trade

7.4.3 General Data Protection Regulations

CHAPTER 8. News Feed Ad and Native Ad

8.1 Status Quo and Challenges in Mobile Advertising

8.1.1 Characteristics of Mobile Advertising

8.1.2 Traditional Creative of Mobile Advertising

8.1.3 Challenges in Front of Mobile Advertising

8.2 News Feed Ad

8.2.1 Definition of News Feed Ad

8.2.2 Key Points about News Feed Ad

8.3 Other Native Ad-Related Products

8.3.1 Search Ad

8.3.2 Advertorial

8.3.3 Affiliate network

8.4 Native Advertising Platform

8.4.1 Native Display and Native Scenario

8.4.2 Scenario Perception and Application

8.4.3 Product Placement Native Ad

8.4.4 Product Cases

8.5 Native Ad and Programmatic Trade

PART 3: Key Technologies for Computational Advertising

CHAPTER 9. Technological Overview

9.1 Personalized System Framework

9.2 Optimization Goals of Various Advertising Systems

9.3 Computational Advertising System Architecture

9.3.1 Ad Serving Engine

9.3.2 Data Highway

9.3.3 Offline Data Processing

9.3.4 Online Data Processing

9.4 Main Technologies for Computational Advertising System

9.5 Build a Computational Advertising System with Open Source Tools

9.5.1 Web Server Nginx

9.5.2 ZooKeeper: Distributed Configuration and Cluster Management Tool

9.5.3 Lucene: Full-Text Retrieval Engine

9.5.4 Thrift: Cross-Language Communication Interface

9.5.5 Data Highway

9.5.6 Hadoop: Distributed Data-Processing Platform

9.5.7 Redis: Online Cache of Features

9.5.8 Strom: Stream Computing Platform Storm

9.5.9 Spark: Efficient Iterative Computing Framework

CHAPTER 10. Fundamental Knowledge

10.1 Information Retrieval

10.1.1 Inverted Index

10.1.2 Vector Space Model

10.2 Optimization

10.2.1 Lagrange Multiplier and Convex Optimization

10.2.2 Downhill Simplex Method

10.2.3 Gradient Descent

10.2.4 Quasi-Newton Methods

10.2.5 Trust Region Method

10.3 Statistical Machine Learning

10.3.1 Maximum Entropy and Exponential Family Distribution

10.3.2 Mixture Model and EM Algorithm

10.3.3 Bayesian Learning

10.4 Distributed Optimization Framework for Statistical Model

10.5 Deep Learning

10.5.1 DNN Optimization Methods

10.5.2 Convolutional Neural Network

10.5.3 Recursive Neural Network

10.5.4 Generative Adversarial Nets

CHAPTER 11. Agreement-Based Advertising Technologies

11.1 Advertising Scheduling System

11.1.1 Scheduling and Mixed Ad Serving

11.2 GD System

11.2.1 Traffic Forecasting

11.2.2 Frequency Capping

11.3 Online Allocation

11.3.1 Online Allocation Problem

11.3.2 Examples of Online Allocation Problems

11.3.3 Limit Performance Analysis

11.3.4 Practical Optimization Algorithms

11.4 Heuristic Allocation Plan HWM

CHAPTER 12. Audience-Targeting Technologies

12.1 Classification of Audience Targeting Technologies

12.2 Contextual Targeting

12.2.1 Near-Line Crawling System

12.3 Text Topic Mining

12.3.1 LSA Model

12.3.2 PLSI Model

12.3.3 LDA Model

12.3.4 Word Embedding (Word2vec)

12.4 Behavioral Targeting

12.4.1 Modeling Problem for Behavioral Targeting

12.4.2 Feature Generation for Behavioral Targeting

12.4.2.1 Tagging Methods for Various Behaviors

12.4.3 Decision-making Process for Behavioral Targeting

12.4.4 Evaluation of Behavioral Targeting

12.5 Prediction of Demographical Attributes

12.6 Data Management Platform

CHAPTER 13. Auction-Based Advertising Technologies

13.1 Pricing Algorithms in Auction-Based Advertising

13.2 Search Ad System

13.2.1 Query Expansion

13.2.2 Ad Placement

13.3 ADN

13.3.1 Short-Term Behavior Feedback and Stream Computing

13.4 Ad Retrieval

13.4.1 Boolean Expression

13.4.2 Relevance Retrieval

13.4.3 DNN-Based Semantic Modeling

13.4.4 ANN Semantic Retrieval

CHAPTER 14. CTR Prediction Model

14.1 CTR Prediction

14.1.1 CTR Basic Model

14.1.2 LR Model-Based Optimization Algorithm

14.1.3 Correction of CTR Model

14.1.4 Features of CTR Model

14.1.5 Evaluation of CTR Model

14.1.6 Intelligent Frequency Capping

14.2 Other CTR Models

14.2.1 Factorization Machines

14.2.2 GBDT

14.2.3 Deep Learning-Based CTR Model

14.3 Exploration and Utilization

14.3.1 Reinforcement Learning and E&E

14.3.2 UCB

14.3.3 Contextual Bandit

CHAPTER 15. Programmatic Trade Technologies

15.1 ADX

15.1.1 Cookie Mapping

15.1.2 Call-out Optimization

15.2 DSP

15.2.1 Customized User Segmentation

15.2.1.1 Look-Alike Modeling

15.2.2 CTR Prediction in DSP

15.2.3 Estimation of Click Value

15.2.4 Bidding Strategy

15.3 SSP

15.3.1 Network Optimization

CHAPTER 16. Other Advertising Technologies

16.1 Creative Optimization

16.1.1 Programmatic Creative

16.1.2 Click Heat Map

16.1.3 Trend of Creative

16.2 Experimental Framework

16.3 Advertising Monitoring and Attribution

16.3.1 Ad Monitoring

16.3.2 Ad Safety

16.3.3 Attribution of Advertising Performance

16.4 Spam and Anti-Spam

16.4.1 Classification of Spam Methods

16.4.2 Common Ad Spam Methods

16.5 Product and Technology Selection

16.5.1 Best Practices for Media

16.5.2 Best Practices for Advertisers

16.5.3 Best Practices for Data Providers

PART 4: Terminology and Index

References

INDEX

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