在线广告投放系统及技术的演变

摘要/Abstract

摘要： 通过对在线广告市场的背景及相关技术的介绍，从广告的形式和投放位置这两个维度对在线广告进行划分，给出了涵盖在线广告领域现有及潜在广告形态的分类体系，分析了不同形态广告投放技术的差异.通过对在线广告投放平台演变和发展的了解，概述了以广告服务器、广告网络、广告交换平台为主的广告投放系统的架构及技术演变，为计算广告相关研究工作提供了关于在线广告投放系统的背景参考和技术概览.

关键词: 在线广告, 计算广告学, 投放系统

Abstract: This paper introduced the background and related techniques of online advertising market. While different types of online ads may imply different computational advertising techniques in the backend system, this paper provided a rational classification method of online advertising formats. The category of online advertising given in this paper covers existing and potential types in the domain. Further, the advertising platform have evolved in several major phases or generations, particularly ad server, ad network and ad exchange. We showed the system architectures of the advertising systems mentioned above, by discussing the main function modules and interfaces. Our work aims to give a comprehensive and detailed description of the online advertising systems from the view of computation. Besides, the surveys in our work can provide essential background knowledge for computational advertising related research.

Key words: online advertising, computational advertising, delivery system

中图分类号:

TP391

宋乐怡，宫学庆，张蓉，刘鹏. 在线广告投放系统及技术的演变[J]. 华东师范大学学报(自然科学版), 2013, 2013(3): 106-117.

SONG Le-yi, GONG Xue-qing, ZHANG Rong, LIU Peng. Online advertising systems and related technology evolution[J]. Journal of East China Normal University(Natural Sc, 2013, 2013(3): 106-117.

参考文献

［1］ Internet Advertising Bureau.IAB internet advertising revenue report［R/OL］. 2012［2013-03-01］.http://www.iab.net/AdRevenueReport..

［2］ ZEFF R L, ARONSON B. Advertising on the Internet［M］. 2nd ed. New York: John Wiley & Sons, 1999.

［3］ BRODER A. Computational Advertising［C］//Proceedings of the 19thAnnual ACM-SIAM Symposium on Discrete Algorithms, SODA. San Francisco, California: SIAM, 2008: 992.

［4］周傲英, 周敏奇, 宫学庆. 计算广告：以数据为核心的Web综合应用［J］. 计算机学报, 2011, 34(10): 1805-1819.

［5］ BRODER A Z. Computational advertising and recommender systems［C］//Proceedings of the 2008 ACM conference on Recommender Systems. ACM, 2008: 1-2.

［6］ HILLARD D, SCHROEDL S, MANAVOGLU E, et al. Improving ad relevance in sponsored search［C］//Proceedings of the 3rdACM International Conference on WebSearch and Data Mining. New York: ACM, 2010: 361-370.

［7］ TOUBIANA V, NARAYANAN A. Adnostic: Privacy Preserving Targeted Advertising［C］//17th Network and Distributed System Security Symposium. 2010: 1-23.

［8］ PANDEY S, ALY M, BAGHERJEIRAN A, et al. Learning to Target: What Works for Behavioral Targeting［C］//Proceedings of the 20thACM International Conference on Information and Knowledge Management. Glasgow, Scotland: 2011: 1805-1814.

［9］ CHEN Y, PAVLOV D, CANNY J. Large-scale behavioral targeting［C］//Proceedings of the 15thACM SIGKDD - International Conference on Knowledge Discovery and Data Mining. Paris: 2009: 209-217.

［10］ CHENG H, CANTP E. Personalized click prediction in sponsored search［C］//Proceedings of the 3rdACM international conference on Web search and data mining. New York, New York: ACM Press, 2010: 351-359.

［11］ REGELSON M, FAIN D. Predicting click-through rate using keyword clusters［C］//Proceedings of the 2ndWorkshop on Sponsored Search Auctions, EC'06. 2006.

［12］ RICHARDSON M, DOMINOWSKA E, RAGNO R. Predicting clicks: estimating the click-through rate for new ads［C］//Proceedings of the 16thinternational conference on World Wide Web. Banff, Alberta, Canada: ACM, 2007: 521-529.

［13］ GRAEPEL T, CANDELA J Q, BORCHERT T, et al. Web-Scale Bayesian Click-Through Rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine［C］//Proceeding of the 27th International Conference on Machine Learning. Haifa, Israel:［s.n.］, 2010.

［14］ CUI Y, ZHANG R, LI W, et al. Bid Landscape Forecasting in Online Ad Exchange Marketplace［C］//Proceedings of the 17thACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Diego, California: ACM, 2011: 265-273.

［15］ DAVE V, GUHA S, ZHANG Y. Measuring and fingerprinting click-spam in ad networks［C］//Proceedings of the ACM SIGCOMM 2012 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication. Helsinki, Finland, 2012: 175-186.

［16］ Google. 广告流量质量资源中心［EB/OL］.［2013-03-01］.http://www.google.com/ads/adtrafficquality/.

［17］ TUZHILIN A. The Lane’s Gifts v. Google Report［R/OL］. 2006［2013-03-01］.http://googleblog.blogspot.com/pdf/Tuzhilin-Report.pdf.

［18］ HGER M, LANDERGREN T. Implementing best practices for fraud detection on an online advertising platform［D］. Gothenburg:Chalmers University of Technology, 2010.

［19］ HADDADI H. Fighting Online Click-Fraud Using Bluff Ads［J］. Computer Communication Review, 2010, 40(2): 21-25.

［20］ ZHANG L, GUAN Y. Detecting Click Fraud in Pay-Per-Click Streams of Online Advertising Networks［C］//The 28thInternational Conference on Distributed Computing Systems. 2008: 77-84.

［21］ METWALLY A, AGRAWAL D, ABBADI A E. DETECTIVES: DETEcting Coalition hiT Inflation attacks in adVertisingnEtworks Streams［C］//Proceeding of the 16thinternational conference on World Wide Web. Banff, Alberta, Canada: 2007: 241-250.

［22］ METWALLY A, AGRAWAL D, ABBADI A El. Using Association Rules for Fraud Detection in Web Advertising Networks［C］//Proceedings of the International Conference on Very Large Data Bases.2005: 169-180.

［23］ DAVE K. Computational Advertising: Leveraging User Interaction & Contextual Factors for Improved Ad Retrieval & Ranking［C］//Proceedings of the 20th International Conference on World Wide Web. Hyderabad, India: 2011: 349-353.

［24］ HA L. Crossing offline and online media: a comparison of online advertising on TV web sites and online portals［J］. Journal of Interactive Advertising, 2003, 3(2): 24-35.

［25］ BRODER A, CICCOLO P, GABRILOVICH E, et al. Online expansion of rare queries for sponsored search［C］//Proceedings of the 18th international conference on world wide web. Madrid, Spain: ACM, 2009: 511-520.

［26］ COLLINS-RECTOR M, SHAKLEY C. Targeting advertising using web pages with video: US, 006188398B1［P］.US Patent 6,188,398, 2001.

［27］ AUXIER R, KHERA V, SEIDMAN C. System and method for increasing click through rates of internet banner advertisements: US, 006379251B1［P］.US Patent 6,379,251, 2002.

［28］ BRODER A, FONTOURA M, JOSIFOVSKI V, et al. A semantic approach to contextual advertising［C］//Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.Amsterdam: ACM, 2007: 559-566.

［29］ NIU X, MA J, ZHANG D. A Survey of Contextual Advertising［C］// Sixth International Conference on Fuzzy Systems and Knowledge Discovery. IEEE, 2009: 505-509.

［30］ Wikipedia.Display advertising［EB/OL］.http://en.wikipedia.org/wiki/Display_advertising.

［31］ LANG K, DELGADO J, JIANG D, et al. Efficient online ad serving in a display advertising exchange［C］//Proceedings of 4th ACM International Conference on Web Search and Data Mining. 2011: 307-316.

［32］ DoubleClick Rich Media Help.Doubleclick. What is Rich Media? ［EB/OL］. 

［33］ MALHEIROS M, JENNETT C, PATEL S, et al. Too Close for Comfort:A Study of the Effectiveness and Acceptability of Rich-Media Personalized Advertising［C］//Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Austin, Texas: ACM, 2012: 579-588.

［34］ PROVOST F, DALESSANDRO B, HOOK R. Audience Selection for On-line Brand Advertising: Privacy-friendly Social Network Targeting［C］//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Paris: ACM, 2009: 707-715.

［35］ BAGHERJEIRAN A, PAREKH R. Combining behavioral and social network data for online advertising［C］//2008 IEEE International Conference on Data Mining Workshops. 2008: 837-846.

［36］ Wikipedia.Classified advertising ［EB/OL］［2013-03-01］.http://en.wikipedia.org/wiki/Classified_advertising.

［37］ BECKER H, BRODER A, GABRILOVICH E, et al. What Happens after an Ad Click? Quantifying the Impact of Landing Pages in Web Advertising［C］//Proceedings of the 18th ACM conference on Information and Knowledge Management. Hong Kong: ACM, 2009: 57-66.

［38］ GE Y, LIU Q, XIONG H, et al. Cost-Aware Travel Tour Recommendation［C］//Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Diego, California: ACM Press, 2011: 983-991.

［39］ Microsoft. Xbox Advertising［EB/OL］［2013-03-01］.http://advertising.microsoft.com/international/xbox-live.

［40］ SENGAMEDU S H, SAWANT N, WADHWA S. vADeo: video advertising system［C］//Proceedings of the 15th international conference on Multimedia. ACM, 2007: 455-456.

［41］ CHEN P, MA W, MANDALAPU S, et al. Ad Serving Using a Compact Allocation Plan［C］//Proceedings of the 13th ACM Conference on Electronic Commerce. 2012, 1(212): 319-336.

［42］ DCCI互联网数据中心. 中国广告网络蓝皮书 Bluebook of Ad Network Services［R/OL］. 2011［2013-04-15］.http://www.docci.com.cn/report.

［43］ WANG Y, BURGENER D, KUZMANOVIC A, et al. Understanding the Network and User-Targeting Properties of Web Advertising Networks［C］//International Conference on Distributed Computing Systems. IEEE, 2011: 613-622.

［44］ OpenX. Ad Networks vs. Ad Exchanges: How They Stack Up - An OpenX whitepaper［R/OL］. 2010［2013-04-15］.http://www.openx.com.

［45］ MUTHUKRISHNAN S. Ad Exchanges: Research Issues［C］//Proceedings of the 5th International Workshop on Internet and Network Economics.［S.l.］: Springer, 2009: 1-12.

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