Computational advertising: market and technologies for internet commercial monetization
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| Main Authors: | , |
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| Format: | Book |
| Language: | English |
| Published: |
Boca Raton
CRC Press
2020
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| Edition: | Second edition |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XLIII, 397 Seiten Illustrationen, Diagramme |
| ISBN: | 9780367206383 |
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| adam_text | 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 Chapter 1 1.1 1.2 FREE MODE AND CORE ASSETS OF THE INTERNET RELATIONSHIP BETWEEN BIG DATA AND ADVERTISING 1.3 1.4 DEFINITION AND PURPOSE OF ADVERTISING PRESENTATION FORMS OF ONLINE ADVERTISING 1.5 BRIEF HISTORY OF ONLINE ADVERTISING Chapter 2 2Л ■ Overview of Online Advertising 1 3 4 5 8 10 18 ■ Basis for Computational Advertising_______________________ 25 ADVERTISING EFFECTIVENESS THEORY 26 2.2 TECHNICAL FEATURES OF THE INTERNET ADVERTISING 29 2.3 CORE ISSUE OF COMPUTATIONAL ADVERTISING 30 2.3.1 2.3.2 Breakdown of Advertising Return Relationship between Billing Models and eCPM Estimation 32 33
xiv ■ Contents 2.4 BUSINESS ORGANIZATIONS IN THE ONLINE ADVERTISING INDUSTRY 2.4.1 2.4.2 2.4.3 Part 2 Chapter 3 Interactive Advertising Bureau American Association of Advertising Agencies Association of National Advertisers Product Logic of Online Advertising . Overview of Online Advertising Products 36 37 38 38 39 41 3.1 DESIGN PHILOSOPHY FOR COMMERCIAL PRODUCTS 43 3.2 PRODUCT INTERFACE OF ADVERTISING SYSTEM 44 3.2.1 3.2.2 3.2.3 Demand-Side Management Interface Supply-Side Management Interface Multiple Forms of Interface between Supply and Demand Sides Chapter 4 ■ Agreement-Based Advertising 44 47 48 51 4.1 AD SPACE AGREEMENT 52 4.2 AUDIENCE TARGETING 53 4.2.1 4.2.2 4.2.3 4.3 Overview of Audience Targeting Technologies Audience Targeting Tag System Design Principles for Tag System DISPLAY QUANTITY AGREEMENT 4.3.1 4.3.2 4.3.3 4.3.4 Traffic Forecasting Traffic Shaping Online Allocation Product Cases 4.3.4.1 Chapter 5 5.1 60 61 61 62 63 63 ■ Search Ad and Auction-Based Advertising__________________ 65 SEARCH AD 5.1.1 5.1.2 5.1.3 5.1.4 5.2 Yahoo! GD 54 57 59 Products of Search Advertising New Forms of Search Ads Product Strategy of Search Advertising Product Cases POSITION AUCTION AND MECHANISM DESIGN 5.2.1 5.2.2 Market Reserve Price Pricing Problem 67 67 70 73 76 79 80 81
Contents ■ 5.2.3 5.2.4 5.2.5 5.3 AUCTION-BASED ADN 5.3.1 5.3.2 5.3.3 5.4 Forms of ADN Products Product Strategy for ADN Product Cases DEMAND-SIDE PRODUCTS IN AUCTION-BASED ADVERTISING 5.4.1 5.4.2 5.4.3 5.5 Squashing Myerson Optimal Auction Examples of Pricing Results Search Engine Marketing Trading Desk Product Cases COMPARISON BETWEEN AUCTION-BASED AND AGREEMENT-BASED ADVERTISING XV 83 84 85 85 86 88 89 90 90 91 91 93 Chapter 6 « Programmatic Trade Advertising__________________________ 95 6.1 RTB 6.1.1 6.2 Product Samples DEMAND-SIDE PLATFORM 6.4.1 6.4.2 6.4.3 6.4.4 6.4.5 6.4.6 6.5 Preferred Deal Private Marketplace Programmatic Direct Buy Spectrum of Advertising Transactions AD EXCHANGE 6.3.1 6.4 RTB Process OTHER MODES OF PROGRAMMED TRADE 6.2.1 6.2.2 6.2.3 6.2.4 6.3 97 DSP Product Strategy Bidding Strategy Bidding and Pricing Processes Retargeting Look-Alike Product Cases SUPPLY-SIDE PLATFORM 6.5.1 6.5.2 6.5.3 SSP Product Strategy Header Bidding Product Cases 98 100 100 101 102 103 104 104 105 106 106 108 108 111 112 113 114 115 117
xvi ■ Contents Chapter 7 ■ Data Processing and Exchange 119 7.1 VALUABLE DATA SOURCES 120 7.2 DATA MANAGEMENT PLATFORM 123 7.2.1 7.2.2 7.2.3 7.2.4 Tripartite Data Partitioning First-Party DMP Third-Party DMP Product Cases 123 123 124 125 7.3 BASIC PROCESS OF DATA TRADING 129 7.4 PRIVACY PROTECTION AND DATA SECURITY 131 7.4.1 7.4.2 7.4.3 Privacy Protection Data Security in Programmatic Trade General Data Protection Regulations Chapter 8 ■ News Feed Ad and Native Ad 8.1 STATUS QUO AND CHALLENGES IN MOBILE ADVERTISING 8.1.1 8.1.2 8.1.3 8.2 NEWS FEED AD 8.2.1 8.2.2 8.3 Part 3 Search Ad Advertorial Affiliate network NATIVE ADVERTISING PLATFORM 8.4.1 8.4.2 8.4.3 8.4.4 8.5 Definition of News Feed Ad Key Points about News Feed Ad OTHER NATIVE AD-RELATED PRODUCTS 8.3.1 8.3.2 8.3.3 8.4 Characteristics of Mobile Advertising Traditional Creative of Mobile Advertising Challenges in Front of Mobile Advertising Native Display and Native Scenario Scenario Perception and Application Product Placement Native Ad Product Cases 139 140 141 142 144 146 146 149 150 150 151 151 151 152 153 154 157 NATIVE AD AND PROGRAMMATIC TRADE 161 Key Technologies for Computational Advertising 163 Chapter 9 ■ Technological Overview 9.1 131 134 136 PERSONALIZED SYSTEM FRAMEWORK 165 166
Contents ■ xvii 9.2 OPTIMIZATION GOALS OF VARIOUS ADVERTISING SYSTEMS 167 9.3 COMPUTATIONAL ADVERTISING SYSTEMARCHITECTURE 169 9.3.1 9.3.2 9.3.3 9.3.4 9.4 9.5 174 BUILD A COMPUTATIONAL ADVERTISING SYSTEM WITH OPEN SOURCE TOOLS 175 9.5.3 9.5.4 9.5.5 9.5.6 9.5.7 9.5.8 9.5.9 Chapter 10 10.2 10.3 10.4 169 172 172 173 MAIN TECHNOLOGIES FOR COMPUTATIONAL ADVERTISING SYSTEM 9.5.1 9.5.2 10.1 Ad Serving Engine Data Highway Offline Data Processing Online Data Processing Web Server Nginx ZooKeeper: Distributed Configuration and Cluster Management Tool Lucene: Full-Text Retrieval Engine Thrift: Cross-Language Communication Interface Data Highway Hadoop: Distributed Data-Processing Platform Redis: Online Cache of Features Strom: Stream Computing PlatformStorm Spark: Efficient Iterative Computing Framework 176 178 179 179 180 181 182 182 183 . Fundamental Knowledge____________________________ 185 INFORMATION RETRIEVAL 186 10.1.1 10.1.2 186 189 Inverted Index Vector Space Model OPTIMIZATION 190 10.2.1 10.2.2 10.2.3 10.2.4 10.2.5 191 192 193 195 199 Lagrange Multiplier and Convex Optimization Downhill Simplex Method Gradient Descent Quasi-Newton Methods Trust Region Method STATISTICAL MACHINE LEARNING 201 10.3.1 10.3.2 10.3.3 202 204 206 Maximum Entropy and Exponential Family Distribution Mixture Model and EM Algorithm Bayesian Learning DISTRIBUTED OPTIMIZATION FRAMEWORK FOR STATISTICAL MODEL 210
xviii ■ Contents 10.5 DEEP LEARNING 211 10.5.1 10.5.2 10.5.3 10.5.4 212 214 215 217 Chapter 11 11.1 . Agreement-Based Advertising Technologies ADVERTISING SCHEDULING SYSTEM 11.1.1 11.2 11.3 11.4 DNN Optimization Methods Convolutional Neural Network Recursive Neural Network Generative Adversarial Nets Scheduling and Mixed Ad Serving 219 220 220 GD SYSTEM 221 11.2.1 11.2.2 222 224 Traffic Forecasting Frequency Capping ONLINE ALLOCATION 227 11.3.1 11.3.2 11.3.3 11.3.4 228 230 232 233 Online Allocation Problem Examples of Online Allocation Problems Limit Performance Analysis Practical Optimization Algorithms HEURISTIC ALLOCATION PLAN HWM Chapter 12 ■ Audience-Targeting Technologies 240 245 12.1 CLASSIFICATION OF AUDIENCE TARGETING TECHNOLOGIES 246 12.2 CONTEXTUAL TARGETING 248 12.2.1 249 12.3 12.4 Near-Line Crawling System TEXT TOPIC MINING 250 12.3.1 12.3.2 12.3.3 12.3.4 250 251 252 253 LSA Model PLSI Model LDA Model Word Embedding (Word2vec) BEHAVIORAL TARGETING 255 12.4.1 12.4.2 Modeling Problem for Behavioral Targeting Feature Generation for Behavioral Targeting 255 257 12.4.2.1 260 12.4.3 12.4.4 Tagging Methods for Various Behaviors Decision-making Process for Behavioral Targeting Evaluation of Behavioral Targeting 261 262 12.5 PREDICTION OF DEMOGRAPHICAL ATTRIBUTES 264 12.6 DATA MANAGEMENT PLATFORM 266
Contents ■ xix Chapter 13 « jAuction-Based Advertising Technologies 267 13.1 PRICING ALGORITHMS IN AUCTION-BASED ADVERTISING 268 13.2 SEARCH AD SYSTEM 270 13.2.1 13.2.2 13.3 Short-Term Behavior Feedback and Stream Computing Boolean Expression Relevance Retrieval DNN-Based Semantic Modeling ANN Semantic Retrieval Chapter 14 CTR Prediction Model 14.1 CTR PREDICTION 14.1.1 14.1.2 14.1.3 14.1.4 14.1.5 14.1.6 14.2 14.3 CTR Basic Model LR Model-Based Optimization Algorithm Correction of CTR Model Features of CTR Model Evaluation of CTR Model Intelligent Frequency Capping OTHER CTR MODELS 14.2.1 14.2.2 14.2.3 Factorization Machines GBDT Deep Learning-Based CTR Model EXPLORATION AND UTILIZATION 14.3.1 14.3.2 14.3.3 Reinforcement Learning and E E UCB Contextual Bandit Chapter 15 · Programmatic Trade Technologies 15.1 ADX 15.1.1 15.1.2 15.2 279 283 288 292 301 302 302 303 312 313 319 321 322 322 323 324 326 327 329 329 331 332 Cookie Mapping Call-out Optimization 334 336 338 DSP 15.2.1 275 278 AD RETRIEVAL 13.4.1 13.4.2 13.4.3 13.4.4 272 274 275 ADN 13.3.1 13.4 Query Expansion Ad Placement Customized User Segmentation 340 15.2.1.1 341 Look-Alike Modeling
xx ■ Contents 15.2.2 15.2.3 15.2.4 15.3 CTR Prediction in DSP Estimation of Click Value Bidding Strategy SSP 15.3.1 345 Network Optimization Chapter 16 ■ Other Advertising Technologies 16.1 342 343 344 346 347 CREATIVE OPTIMIZATION 348 16.1.1 16.1.2 16.1.3 349 350 351 Programmatic Creative Click Heat Map Trend of Creative 16.2 EXPERIMENTAL FRAMEWORK 353 16.3 ADVERTISING MONITORING AND ATTRIBUTION 354 16.3.1 16.3.2 16.3.3 355 356 357 16.4 16.5 Part 4 Ad Monitoring Ad Safety Attribution of Advertising Performance SPAM AND ANTI-SPAM 359 16.4.1 16.4.2 Classification of Spam Methods Common Ad Spam Methods 359 360 PRODUCT AND TECHNOLOGY SELECTION 366 16.5.1 16.5.2 16.5.3 367 370 372 Best Practices for Media Best Practices for Advertisers Best Practices for Data Providers Terminology and Index REFERENCES, 381 INDEX, 387 375
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| adam_txt |
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 Chapter 1 1.1 1.2 FREE MODE AND CORE ASSETS OF THE INTERNET RELATIONSHIP BETWEEN BIG DATA AND ADVERTISING 1.3 1.4 DEFINITION AND PURPOSE OF ADVERTISING PRESENTATION FORMS OF ONLINE ADVERTISING 1.5 BRIEF HISTORY OF ONLINE ADVERTISING Chapter 2 2Л ■ Overview of Online Advertising 1 3 4 5 8 10 18 ■ Basis for Computational Advertising_ 25 ADVERTISING EFFECTIVENESS THEORY 26 2.2 TECHNICAL FEATURES OF THE INTERNET ADVERTISING 29 2.3 CORE ISSUE OF COMPUTATIONAL ADVERTISING 30 2.3.1 2.3.2 Breakdown of Advertising Return Relationship between Billing Models and eCPM Estimation 32 33
xiv ■ Contents 2.4 BUSINESS ORGANIZATIONS IN THE ONLINE ADVERTISING INDUSTRY 2.4.1 2.4.2 2.4.3 Part 2 Chapter 3 Interactive Advertising Bureau American Association of Advertising Agencies Association of National Advertisers Product Logic of Online Advertising . Overview of Online Advertising Products 36 37 38 38 39 41 3.1 DESIGN PHILOSOPHY FOR COMMERCIAL PRODUCTS 43 3.2 PRODUCT INTERFACE OF ADVERTISING SYSTEM 44 3.2.1 3.2.2 3.2.3 Demand-Side Management Interface Supply-Side Management Interface Multiple Forms of Interface between Supply and Demand Sides Chapter 4 ■ Agreement-Based Advertising 44 47 48 51 4.1 AD SPACE AGREEMENT 52 4.2 AUDIENCE TARGETING 53 4.2.1 4.2.2 4.2.3 4.3 Overview of Audience Targeting Technologies Audience Targeting Tag System Design Principles for Tag System DISPLAY QUANTITY AGREEMENT 4.3.1 4.3.2 4.3.3 4.3.4 Traffic Forecasting Traffic Shaping Online Allocation Product Cases 4.3.4.1 Chapter 5 5.1 60 61 61 62 63 63 ■ Search Ad and Auction-Based Advertising_ 65 SEARCH AD 5.1.1 5.1.2 5.1.3 5.1.4 5.2 Yahoo! GD 54 57 59 Products of Search Advertising New Forms of Search Ads Product Strategy of Search Advertising Product Cases POSITION AUCTION AND MECHANISM DESIGN 5.2.1 5.2.2 Market Reserve Price Pricing Problem 67 67 70 73 76 79 80 81
Contents ■ 5.2.3 5.2.4 5.2.5 5.3 AUCTION-BASED ADN 5.3.1 5.3.2 5.3.3 5.4 Forms of ADN Products Product Strategy for ADN Product Cases DEMAND-SIDE PRODUCTS IN AUCTION-BASED ADVERTISING 5.4.1 5.4.2 5.4.3 5.5 Squashing Myerson Optimal Auction Examples of Pricing Results Search Engine Marketing Trading Desk Product Cases COMPARISON BETWEEN AUCTION-BASED AND AGREEMENT-BASED ADVERTISING XV 83 84 85 85 86 88 89 90 90 91 91 93 Chapter 6 « Programmatic Trade Advertising_ 95 6.1 RTB 6.1.1 6.2 Product Samples DEMAND-SIDE PLATFORM 6.4.1 6.4.2 6.4.3 6.4.4 6.4.5 6.4.6 6.5 Preferred Deal Private Marketplace Programmatic Direct Buy Spectrum of Advertising Transactions AD EXCHANGE 6.3.1 6.4 RTB Process OTHER MODES OF PROGRAMMED TRADE 6.2.1 6.2.2 6.2.3 6.2.4 6.3 97 DSP Product Strategy Bidding Strategy Bidding and Pricing Processes Retargeting Look-Alike Product Cases SUPPLY-SIDE PLATFORM 6.5.1 6.5.2 6.5.3 SSP Product Strategy Header Bidding Product Cases 98 100 100 101 102 103 104 104 105 106 106 108 108 111 112 113 114 115 117
xvi ■ Contents Chapter 7 ■ Data Processing and Exchange 119 7.1 VALUABLE DATA SOURCES 120 7.2 DATA MANAGEMENT PLATFORM 123 7.2.1 7.2.2 7.2.3 7.2.4 Tripartite Data Partitioning First-Party DMP Third-Party DMP Product Cases 123 123 124 125 7.3 BASIC PROCESS OF DATA TRADING 129 7.4 PRIVACY PROTECTION AND DATA SECURITY 131 7.4.1 7.4.2 7.4.3 Privacy Protection Data Security in Programmatic Trade General Data Protection Regulations Chapter 8 ■ News Feed Ad and Native Ad 8.1 STATUS QUO AND CHALLENGES IN MOBILE ADVERTISING 8.1.1 8.1.2 8.1.3 8.2 NEWS FEED AD 8.2.1 8.2.2 8.3 Part 3 Search Ad Advertorial Affiliate network NATIVE ADVERTISING PLATFORM 8.4.1 8.4.2 8.4.3 8.4.4 8.5 Definition of News Feed Ad Key Points about News Feed Ad OTHER NATIVE AD-RELATED PRODUCTS 8.3.1 8.3.2 8.3.3 8.4 Characteristics of Mobile Advertising Traditional Creative of Mobile Advertising Challenges in Front of Mobile Advertising Native Display and Native Scenario Scenario Perception and Application Product Placement Native Ad Product Cases 139 140 141 142 144 146 146 149 150 150 151 151 151 152 153 154 157 NATIVE AD AND PROGRAMMATIC TRADE 161 Key Technologies for Computational Advertising 163 Chapter 9 ■ Technological Overview 9.1 131 134 136 PERSONALIZED SYSTEM FRAMEWORK 165 166
Contents ■ xvii 9.2 OPTIMIZATION GOALS OF VARIOUS ADVERTISING SYSTEMS 167 9.3 COMPUTATIONAL ADVERTISING SYSTEMARCHITECTURE 169 9.3.1 9.3.2 9.3.3 9.3.4 9.4 9.5 174 BUILD A COMPUTATIONAL ADVERTISING SYSTEM WITH OPEN SOURCE TOOLS 175 9.5.3 9.5.4 9.5.5 9.5.6 9.5.7 9.5.8 9.5.9 Chapter 10 10.2 10.3 10.4 169 172 172 173 MAIN TECHNOLOGIES FOR COMPUTATIONAL ADVERTISING SYSTEM 9.5.1 9.5.2 10.1 Ad Serving Engine Data Highway Offline Data Processing Online Data Processing Web Server Nginx ZooKeeper: Distributed Configuration and Cluster Management Tool Lucene: Full-Text Retrieval Engine Thrift: Cross-Language Communication Interface Data Highway Hadoop: Distributed Data-Processing Platform Redis: Online Cache of Features Strom: Stream Computing PlatformStorm Spark: Efficient Iterative Computing Framework 176 178 179 179 180 181 182 182 183 . Fundamental Knowledge_ 185 INFORMATION RETRIEVAL 186 10.1.1 10.1.2 186 189 Inverted Index Vector Space Model OPTIMIZATION 190 10.2.1 10.2.2 10.2.3 10.2.4 10.2.5 191 192 193 195 199 Lagrange Multiplier and Convex Optimization Downhill Simplex Method Gradient Descent Quasi-Newton Methods Trust Region Method STATISTICAL MACHINE LEARNING 201 10.3.1 10.3.2 10.3.3 202 204 206 Maximum Entropy and Exponential Family Distribution Mixture Model and EM Algorithm Bayesian Learning DISTRIBUTED OPTIMIZATION FRAMEWORK FOR STATISTICAL MODEL 210
xviii ■ Contents 10.5 DEEP LEARNING 211 10.5.1 10.5.2 10.5.3 10.5.4 212 214 215 217 Chapter 11 11.1 . Agreement-Based Advertising Technologies ADVERTISING SCHEDULING SYSTEM 11.1.1 11.2 11.3 11.4 DNN Optimization Methods Convolutional Neural Network Recursive Neural Network Generative Adversarial Nets Scheduling and Mixed Ad Serving 219 220 220 GD SYSTEM 221 11.2.1 11.2.2 222 224 Traffic Forecasting Frequency Capping ONLINE ALLOCATION 227 11.3.1 11.3.2 11.3.3 11.3.4 228 230 232 233 Online Allocation Problem Examples of Online Allocation Problems Limit Performance Analysis Practical Optimization Algorithms HEURISTIC ALLOCATION PLAN HWM Chapter 12 ■ Audience-Targeting Technologies 240 245 12.1 CLASSIFICATION OF AUDIENCE TARGETING TECHNOLOGIES 246 12.2 CONTEXTUAL TARGETING 248 12.2.1 249 12.3 12.4 Near-Line Crawling System TEXT TOPIC MINING 250 12.3.1 12.3.2 12.3.3 12.3.4 250 251 252 253 LSA Model PLSI Model LDA Model Word Embedding (Word2vec) BEHAVIORAL TARGETING 255 12.4.1 12.4.2 Modeling Problem for Behavioral Targeting Feature Generation for Behavioral Targeting 255 257 12.4.2.1 260 12.4.3 12.4.4 Tagging Methods for Various Behaviors Decision-making Process for Behavioral Targeting Evaluation of Behavioral Targeting 261 262 12.5 PREDICTION OF DEMOGRAPHICAL ATTRIBUTES 264 12.6 DATA MANAGEMENT PLATFORM 266
Contents ■ xix Chapter 13 « jAuction-Based Advertising Technologies 267 13.1 PRICING ALGORITHMS IN AUCTION-BASED ADVERTISING 268 13.2 SEARCH AD SYSTEM 270 13.2.1 13.2.2 13.3 Short-Term Behavior Feedback and Stream Computing Boolean Expression Relevance Retrieval DNN-Based Semantic Modeling ANN Semantic Retrieval Chapter 14 CTR Prediction Model 14.1 CTR PREDICTION 14.1.1 14.1.2 14.1.3 14.1.4 14.1.5 14.1.6 14.2 14.3 CTR Basic Model LR Model-Based Optimization Algorithm Correction of CTR Model Features of CTR Model Evaluation of CTR Model Intelligent Frequency Capping OTHER CTR MODELS 14.2.1 14.2.2 14.2.3 Factorization Machines GBDT Deep Learning-Based CTR Model EXPLORATION AND UTILIZATION 14.3.1 14.3.2 14.3.3 Reinforcement Learning and E E UCB Contextual Bandit Chapter 15 · Programmatic Trade Technologies 15.1 ADX 15.1.1 15.1.2 15.2 279 283 288 292 301 302 302 303 312 313 319 321 322 322 323 324 326 327 329 329 331 332 Cookie Mapping Call-out Optimization 334 336 338 DSP 15.2.1 275 278 AD RETRIEVAL 13.4.1 13.4.2 13.4.3 13.4.4 272 274 275 ADN 13.3.1 13.4 Query Expansion Ad Placement Customized User Segmentation 340 15.2.1.1 341 Look-Alike Modeling
xx ■ Contents 15.2.2 15.2.3 15.2.4 15.3 CTR Prediction in DSP Estimation of Click Value Bidding Strategy SSP 15.3.1 345 Network Optimization Chapter 16 ■ Other Advertising Technologies 16.1 342 343 344 346 347 CREATIVE OPTIMIZATION 348 16.1.1 16.1.2 16.1.3 349 350 351 Programmatic Creative Click Heat Map Trend of Creative 16.2 EXPERIMENTAL FRAMEWORK 353 16.3 ADVERTISING MONITORING AND ATTRIBUTION 354 16.3.1 16.3.2 16.3.3 355 356 357 16.4 16.5 Part 4 Ad Monitoring Ad Safety Attribution of Advertising Performance SPAM AND ANTI-SPAM 359 16.4.1 16.4.2 Classification of Spam Methods Common Ad Spam Methods 359 360 PRODUCT AND TECHNOLOGY SELECTION 366 16.5.1 16.5.2 16.5.3 367 370 372 Best Practices for Media Best Practices for Advertisers Best Practices for Data Providers Terminology and Index REFERENCES, 381 INDEX, 387 375 |
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| dewey-sort | 3658.8 272 |
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| id | DE-604.BV047443690 |
| illustrated | Illustrated |
| index_date | 2024-07-03T18:01:38Z |
| indexdate | 2024-07-10T09:12:17Z |
| institution | BVB |
| isbn | 9780367206383 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032845827 |
| oclc_num | 1277020429 |
| open_access_boolean | |
| owner | DE-739 |
| owner_facet | DE-739 |
| physical | XLIII, 397 Seiten Illustrationen, Diagramme |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | CRC Press |
| record_format | marc |
| spelling | Liu, Peng Verfasser aut Computational advertising market and technologies for internet commercial monetization Liu Peng, Wang Chao Second edition Boca Raton CRC Press 2020 XLIII, 397 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Datenverarbeitung (DE-588)4011152-0 gnd rswk-swf Electronic Commerce (DE-588)4592128-3 gnd rswk-swf Online-Marketing (DE-588)7706419-7 gnd rswk-swf COMPUTERS / General ; bisacsh Internet advertising Online-Marketing (DE-588)7706419-7 s Electronic Commerce (DE-588)4592128-3 s Datenverarbeitung (DE-588)4011152-0 s DE-604 Wang, Chao Verfasser aut Erscheint auch als Online-Ausgabe 0-429-55325-0 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032845827&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Liu, Peng Wang, Chao Computational advertising market and technologies for internet commercial monetization Datenverarbeitung (DE-588)4011152-0 gnd Electronic Commerce (DE-588)4592128-3 gnd Online-Marketing (DE-588)7706419-7 gnd |
| subject_GND | (DE-588)4011152-0 (DE-588)4592128-3 (DE-588)7706419-7 |
| 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 Liu Peng, Wang Chao |
| title_fullStr | Computational advertising market and technologies for internet commercial monetization Liu Peng, Wang Chao |
| title_full_unstemmed | Computational advertising market and technologies for internet commercial monetization Liu Peng, Wang Chao |
| 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 | Datenverarbeitung (DE-588)4011152-0 gnd Electronic Commerce (DE-588)4592128-3 gnd Online-Marketing (DE-588)7706419-7 gnd |
| topic_facet | Datenverarbeitung Electronic Commerce Online-Marketing |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032845827&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT liupeng computationaladvertisingmarketandtechnologiesforinternetcommercialmonetization AT wangchao computationaladvertisingmarketandtechnologiesforinternetcommercialmonetization |