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网络广告定向技术综述

郭心语 刘鹏 周敏奇 周傲英

郭心语, 刘鹏, 周敏奇, 周傲英. 网络广告定向技术综述[J]. 华东师范大学学报(自然科学版), 2013, (3): 93-105.
引用本文: 郭心语, 刘鹏, 周敏奇, 周傲英. 网络广告定向技术综述[J]. 华东师范大学学报(自然科学版), 2013, (3): 93-105.
GUO Xin-yu, LIU Peng, ZHOU Min-qi, ZHOU Ao-ying. Survey of online advertising target[J]. Journal of East China Normal University (Natural Sciences), 2013, (3): 93-105.
Citation: GUO Xin-yu, LIU Peng, ZHOU Min-qi, ZHOU Ao-ying. Survey of online advertising target[J]. Journal of East China Normal University (Natural Sciences), 2013, (3): 93-105.

网络广告定向技术综述

详细信息
  • 中图分类号: TP311

Survey of online advertising target

  • 摘要: 近几年来网络广告异军突起,形式也逐渐多样化.随着网络广告市场的急剧增加,越来越多的广告主希望在推出某产品或服务后,能够用更少的支出吸引更多对该产品或服务感兴趣的用户与广告进行交互(看到广告,点击广告,注册、下订单、购买产品等等后继行为).网络广告定向技术因此应运而生.本文介绍了定向技术的分类,并对每种定向技术具体阐述了其应用场景;总结和对比了近几年网络定向技术中常用的方法和模型.
  • [1] [1]

    GOLDFARB A, TUCKE C.Search engine advertising: channel substitution when pricing ads to context[J]. Management Science, 2011, 57(3): 458-470.

    [2] FAIN D C, PEDEREN J O. Sponsored Search: a Brief History[J]. Computer Science, 2006, 32(2):12-13.

    [3] JANSEN B J, MULLEN T. Sponsored Search: an overview of the concept, history, and technology[J]. Int J Electronic Business, 2008, 6(2):114-131.

    [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] 牛国柱.互联网精准广告定向技术[EB/OL].2012[2013-04-15].http://www.iamniu.com/2012/05/26/summary-internet-precise-ad-targeting-technology/.

    [6] RIBERIRO-NETO B, CRISTO V, GOLGHER  P B, et al.Impedance Coupling in Content-targetedadvertising[C]∥SIGIR'05, 2005.

    [7] LACERDA A, CRISTO  M, GONCALVES  M A. Learn to Advertising[C]∥SIGIR. 2006.

    [8] BRODER A, FONTOURA  M, JOSIFOVSKI  V, et al.A Semantic Approach to Contextual Advertising[C]∥SIGIR 2007: 559-566.

    [9] ANAGNOSTOPOULOS A, BRODER  A Z, GABRILOVICH  E, et al. Just-in-time Contextual Advertising[C]. Proceedings of the sixteenth ACM conference on Conference on information and knowledge management.Lisboa:ACM,2007.

    [10] FAN T K, CHAGN C H. Sentiment-oriented contextual advertising[J].Knowl Inf sys, 2010, 23:321-344.

    [11] KIM S M, HOVY E. Automatic Identification of Pro and Con Reasons in Online Reviews[C]. Proceedings of the COLING/ACL 2006, Main Conference Poster Sessions, 2006: 483-490.

    [12] CIARAMITAM M, MURDOCK  V, PLACHOURAS V.Semantic associations for contextual advertising[J]. Journal of Electronic Commerce Research. 2008,9(1):1-15.

    [13] YIH W -T, GOODMAN  J,  CARVALHO V.Finding Advertising Keywords on Web Pages[C]∥Proceedings of the 15th International World Wide Web Conference (WWW),2006.

    [14] REGELSON M, FAIN D C. Predicting click-through rate using keyword cluster[C]∥7th ACM Conference on Electronic Commerce. 2006.

    [15] RICHARDSON M, DOMINOWSKA E, RAGNO R. Predicting Clicks:Estimating the Click Through Rate for New Ads[C]∥Proceedings of the 16th international conference on World Wide Web(WWW),2007: 521-530.

    [16] CHAKRABARTI D, AGARWA  D, JOSIFOVSKI V. Contextual Advertising by Combining Relevance with Click Feedback[C]∥Proceedings of the 17th international conference on World Wide Web(WWW),2008: 417-426.

    [17] YAN J, LIU  N, WANG  G, et al. How Much can Behavioral Targeting Help Online Advertising[C]∥Proceedings of the 18th International Conference on World Wide Web, 2009.

    [18] SALTON G, BUCKLEY C. Term weighting approaches in automatic text retrieval[J].Information processing & management,1988,24: 513-523.

    [19] CHEN Y, PAVLOV  D, CANNY  J F. Large-scale Behavioral Targeting[C]∥KDD, 2009: 209-218. 

    [20] DEERWESTER S, DUMAIS  S, FURNAS  G, et al. Indexing by Latent Semantic Analysis[J]. Journal of the American Society for Information Science, 1990,41:391-407.

    [21] HOFMANN T.Probabilistic Latent Semantic Analysis[C]∥Proceedings of UAI’99. Stockholm: Morgan Kaufmann, 1999: 289-296.

    [22] HOFMANN T. Unsupervised Learning by Probabilistic LatentSemantic Analysis[J]. Machine Learning Journal, 2001,42:177-196.

    [23] BLEI D, NG  A, JORDAN  M. Latent Dirichlet Allocation[J].Journal of Machine Learning Research, 2003, 3:993-1022.

    [24] Tianqi Chen, Jun Yan, GuirongXue, Zheng Chen. Transfer Learning for Behavioral Targeting[C].In Proceedings of the 15th International World Wide Web Conference (WWW). Pages 1077-1078. 2010.

    [25] A. Ahmed, Y. Low, M. Aly, V. Josifovski, and A. J. Smola.Scalable Distributed Inference of Dynamic User Interests for Behavioral Targeting[C]. In Proceedings of the of the 17th SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011.

    [26] Dakan Wang, Gang Wang, XiaofengKe, WeizhuChen .Action Prediction and Identification from Mining Temporal User Behaviors.  Proceedings of the fourth ACM international conference on Web search and data mining(WSDM). Pages 435-444 . 2011.

    [27] SusanGauch, MircoSperetta, AravindChandramouli, Alessandro Micarelli. User Profiles for Personalized Information Access[J]. Computer Science.2007:54-89.

    [28] Lin Li, Zhenglu Yang, Botao Wang, Masaru Kitsuregawa. Dynamic Adaptation Strategies for Long-term and Short-term User Profile to personalize search[J].Computer Science. 2007: 228-240.

    [29] Hyoung R. Kim,Hyoung R. Kim.Learning Implicit User Interest Hierarchy for Context in Personalization [C]. Proceedings of the 8th international conference on Intelligent user interfaces(IUI). Pages 101-108. 2003.

    [30] Ahmed Hassan, Rosie Jones, Kristina Lisa Klinkner. Beyond DCG: User Behavior as a Predictor of a Successful Search[C]. Proceedings of the third ACM international conference on Web search and data mining(WSDM). Pages 221-230. 2010.

    [31] Ravi Kumar, Andrew Tomkins. A characterization of online search behavior[J].IEEE Data Engineering Bulletin.2009,32(2).

    [32] Lihong Li, Wei Chu, John Langford, Robert E.Schapire.A contextual bandit approach to personalized news article recommendation[C].Proceedings of the 19th international conference on World wide web(WWW). Pages 661-670 .2010.

    [33] K. L. Chang and V. K. Narayanan. Performance Analysis of Behavioral Targeting at yahoo! [R]Technical report, Advertising Sciences, Yahoo! Labs, 2010.

    [34] AymanFarahat, Michael Bailey. How effective is targeted advertising[C]. Proceedings of the 21st international conference on World Wide Web(WWW). Pages 111-120. 2012.

    [35] Ye Chen, Pavlov, Berklin, Canny. Large-scale Behavioral Targeting for Advertising over a Network[p].8150723. 2012. 

    [36] NingLiu,JunYan,DouShen,DepinChen,ZhengChen,YingLi.Learning to Rank Audience for Behavioral Targeting[C]. Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval(SIGIR). Pages 719-720. 2010.

    [37] Xiaohui Wu, Jun Yan, NingLiu,ShuichengYan,YingChen,Zheng Chen. Probabilistic latent semantic user segmentation for behavioral targeted advertising[C].Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising(ADKDD). Pages 10-17.2009.

    [38] Mohamed Aly, Andrew Hatch, VanjaJosifovski, Vijay K. Narayanan.Web-scale user modeling for targeting[C].Proceedings of the 21st international conference companion on World Wide Web(WWW). Pages 3-12 . 2012.

    [39] TingLi,NingLiu,JunYan,GangWang,FengshanBai, Zheng Chen. A Markov chain model for integrating behavioral targeting into contextual advertising[C].Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising(ADKDD). Pages 1-9 . 2009.

    [40] Daisuke Kobayashi,Mitsuru Ishizuka. Automatic Estimation of Bloggers’ Gender [C]. The International Conferences on Weblogs and Social Media(ICWSM). 2006.

    [41] DanMuarry, KevanDurrell.Inferring demographic attributes of anonymous Internet Users[J]. Computer Science. 2000:7-20.

    [42] JansenBJ,Solomon L. Gender Demographic Targeting in Sponsored Search[C]. Proceedings of the 28th International Conference on Human Factors in Computing System.2010.

    [43] Chen, V. (2009, 14 January). Behavioral Targeting and the Gender Divide.Retrieved 1 September, 2009, from http://www.clickz.com/3632342.

    [44] McMahan, C., Hovland, R. and McMillan, S., Gender and Internet Advertising: Differences in the Ways Males and Females Engage and Perceive Internet Advertising[C]. in American Academy of Advertising Conference, (Lubbock, Tx, 2008), 52-55.

    [45] John Lafferty, Andrew McCallum, Fernando C.N. Pereira. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data[J]. Scholarly Commons, 2001.
  • [1] 施沈阳, 葛建忠, 陈建忠, 郑晓琴, 丁平兴.  基于FVCOM的物理—生物地球化学耦合模型构建与应用 . 华东师范大学学报(自然科学版), 2020, (3): 55-67. doi: 10.3969/j.issn.1000-5641.201941008
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出版历程
  • 收稿日期:  2013-03-01
  • 修回日期:  2013-04-01
  • 刊出日期:  2013-05-25

网络广告定向技术综述

  • 中图分类号: TP311

摘要: 近几年来网络广告异军突起,形式也逐渐多样化.随着网络广告市场的急剧增加,越来越多的广告主希望在推出某产品或服务后,能够用更少的支出吸引更多对该产品或服务感兴趣的用户与广告进行交互(看到广告,点击广告,注册、下订单、购买产品等等后继行为).网络广告定向技术因此应运而生.本文介绍了定向技术的分类,并对每种定向技术具体阐述了其应用场景;总结和对比了近几年网络定向技术中常用的方法和模型.

English Abstract

郭心语, 刘鹏, 周敏奇, 周傲英. 网络广告定向技术综述[J]. 华东师范大学学报(自然科学版), 2013, (3): 93-105.
引用本文: 郭心语, 刘鹏, 周敏奇, 周傲英. 网络广告定向技术综述[J]. 华东师范大学学报(自然科学版), 2013, (3): 93-105.
GUO Xin-yu, LIU Peng, ZHOU Min-qi, ZHOU Ao-ying. Survey of online advertising target[J]. Journal of East China Normal University (Natural Sciences), 2013, (3): 93-105.
Citation: GUO Xin-yu, LIU Peng, ZHOU Min-qi, ZHOU Ao-ying. Survey of online advertising target[J]. Journal of East China Normal University (Natural Sciences), 2013, (3): 93-105.
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