Computational Advertising: Market and Technologies for Internet Commercial Monetization
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Milton
Taylor & Francis Group
2020
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| Ausgabe: | 2nd ed |
| Schlagworte: | |
| Online-Zugang: | HWR01 |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 online resource (443 pages) |
| ISBN: | 9780429557729 |
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| 505 | 8 | |a Cover -- Endorsements -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Figures -- Tables -- Foreword -- Preface (1) -- Preface (2) -- Preface (3) -- Authors -- 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 | |
| 505 | 8 | |a 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 -- 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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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| contents | Cover -- Endorsements -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Figures -- Tables -- Foreword -- Preface (1) -- Preface (2) -- Preface (3) -- Authors -- 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 -- 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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| dewey-full | 659.14400000000001 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 659 - Advertising and public relations |
| dewey-raw | 659.14400000000001 |
| dewey-search | 659.14400000000001 |
| dewey-sort | 3659.14400000000001 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Informatik Wirtschaftswissenschaften |
| discipline_str_mv | Informatik Wirtschaftswissenschaften |
| edition | 2nd ed |
| format | Electronic eBook |
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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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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 -- 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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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&amp -- 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</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Internet advertising</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Online-Marketing</subfield><subfield code="0">(DE-588)7706419-7</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Electronic Commerce</subfield><subfield code="0">(DE-588)4592128-3</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Datenverarbeitung</subfield><subfield code="0">(DE-588)4011152-0</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Online-Marketing</subfield><subfield code="0">(DE-588)7706419-7</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Electronic Commerce</subfield><subfield code="0">(DE-588)4592128-3</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Datenverarbeitung</subfield><subfield code="0">(DE-588)4011152-0</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Chao</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="a">Liu, Peng</subfield><subfield code="t">Computational Advertising</subfield><subfield code="d">Milton : Taylor & Francis Group,c2020</subfield><subfield code="z">9780367206383</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-PQE</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-033077537</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://ebookcentral.proquest.com/lib/hwr/detail.action?docID=6196016</subfield><subfield code="l">HWR01</subfield><subfield code="p">ZDB-30-PQE</subfield><subfield code="q">HWR_PDA_PQE</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV047693545 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T18:57:26Z |
| indexdate | 2024-07-10T09:19:20Z |
| institution | BVB |
| isbn | 9780429557729 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033077537 |
| oclc_num | 1154553240 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 online resource (443 pages) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Taylor & Francis Group |
| record_format | marc |
| spelling | Liu, Peng Verfasser aut Computational Advertising Market and Technologies for Internet Commercial Monetization 2nd ed Milton Taylor & Francis Group 2020 ©2020 1 online resource (443 pages) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources Cover -- Endorsements -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Figures -- Tables -- Foreword -- Preface (1) -- Preface (2) -- Preface (3) -- Authors -- 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 -- 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 Internet advertising Online-Marketing (DE-588)7706419-7 gnd rswk-swf Electronic Commerce (DE-588)4592128-3 gnd rswk-swf Datenverarbeitung (DE-588)4011152-0 gnd rswk-swf Online-Marketing (DE-588)7706419-7 s Electronic Commerce (DE-588)4592128-3 s Datenverarbeitung (DE-588)4011152-0 s DE-604 Wang, Chao Sonstige oth Erscheint auch als Druck-Ausgabe Liu, Peng Computational Advertising Milton : Taylor & Francis Group,c2020 9780367206383 |
| spellingShingle | Liu, Peng Computational Advertising Market and Technologies for Internet Commercial Monetization Cover -- Endorsements -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Figures -- Tables -- Foreword -- Preface (1) -- Preface (2) -- Preface (3) -- Authors -- 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 -- 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 Internet advertising Online-Marketing (DE-588)7706419-7 gnd Electronic Commerce (DE-588)4592128-3 gnd Datenverarbeitung (DE-588)4011152-0 gnd |
| subject_GND | (DE-588)7706419-7 (DE-588)4592128-3 (DE-588)4011152-0 |
| title | Computational Advertising Market and Technologies for Internet Commercial Monetization |
| title_auth | Computational Advertising Market and Technologies for Internet Commercial Monetization |
| title_exact_search | Computational Advertising Market and Technologies for Internet Commercial Monetization |
| title_exact_search_txtP | Computational Advertising Market and Technologies for Internet Commercial Monetization |
| title_full | Computational Advertising Market and Technologies for Internet Commercial Monetization |
| title_fullStr | Computational Advertising Market and Technologies for Internet Commercial Monetization |
| title_full_unstemmed | Computational Advertising Market and Technologies for Internet Commercial Monetization |
| title_short | Computational Advertising |
| title_sort | computational advertising market and technologies for internet commercial monetization |
| title_sub | Market and Technologies for Internet Commercial Monetization |
| topic | Internet advertising Online-Marketing (DE-588)7706419-7 gnd Electronic Commerce (DE-588)4592128-3 gnd Datenverarbeitung (DE-588)4011152-0 gnd |
| topic_facet | Internet advertising Online-Marketing Electronic Commerce Datenverarbeitung |
| work_keys_str_mv | AT liupeng computationaladvertisingmarketandtechnologiesforinternetcommercialmonetization AT wangchao computationaladvertisingmarketandtechnologiesforinternetcommercialmonetization |