Journal of East China Normal University(Natural Science) >

2017 , Vol. 2017 >Issue 5: 80 - 86,100

DOI: https://doi.org/10.3969/j.issn.1000-5641.2017.05.008

Big Data Analysis

Study of click through rate prediction in online advertisement

  • XIAO YAO ,
  • BI Jun-fang ,
  • HAN YI ,
  • DONG Qi-wen
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  • 1. School of Data Science and Engineering, East China Normal University, Shanghai 200062, China;
    2. Yangtze River Estuary Survey Bureau of Hydrology and Water Resource, CWRC, Ministry of Water Resources, Shanghai 200136, China

Received date: 2017-05-01

  Online published: 2017-09-25

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Abstract

With the development of the Internet and the growth of users, the advertising industry originated from the traditional offline advertising model, is gradually transforming into online advertising model. At the same time, due to the use of large data analysis technology, online advertising shows great advantages when compared with traditional advertising. The advertisers deliver their advertisements to the platform's specific positions by competition auction of counterparts. Therefore, it is important to predict the click through rate (CTR) of a given advertisement before auction, which is important for advertisers to reduce costs and expand their likely revenue.This paper introduces the commonly used ad click rate prediction model, uses the information from different advertisers, advertisements and media platforms as the features of machine learning, and uses real data sets to illustrate the advantages of various models,and the impact of different features on the ad click rate.

Key words： computational advertising; CTR; machine learning

Cite this article

XIAO YAO , BI Jun-fang , HAN YI , DONG Qi-wen . Study of click through rate prediction in online advertisement[J]. Journal of East China Normal University(Natural Science), 2017 , 2017(5) : 80 -86,100 . DOI: 10.3969/j.issn.1000-5641.2017.05.008

References

[1] GABRILOVICH E. An Overview of Computational Advertising[R/OL].[2013-03-21]. http://research.yahoo.com/pub/2915.
[2] AGARWAL D, CHAKRABARTI D. Statistical Challenge in Online Advertising[R/OL].[2013-03-21]. http://research.yahoo.com/pub/2430.
[3] 纪文迪, 王晓玲, 周傲英. 广告点击率估算技术综述[J]. 华东师范大学学报(自然科学版), 2013(3):2-14.
[4] AGARWAL D, AGRAWAL R, KHANNA R, et al. Estimating rates of rare events with multiple hierarchies through scalable log-linear models[C]//ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2010:213-222.
[5] RICHARDSON M, DOMINOWSKA E, RAGNO R. Predicting clicks:estimating the click-through rate for new ads[C]//International Conference on World Wide Web. ACM, 2007:521-530.
[6] HE X, PAN J, JIN O, et al. Practical Lessons from Predicting Clicks on Ads at Facebook[C]//Eighth International Workshop on Data Mining for Online Advertising. ACM, 2014:1-9.
[7] CHAPELLE O, ZHANG Y. A dynamic bayesian network click model for web search ranking[C]//International Conference on World Wide Web. ACM, 2009:1-10.
[8] DUPRET G E, PIWOWARSKI B. A user browsing model to predict search engine click data from past observations[C]//International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2008:331-338.
[9] DAVE K, VARMA V. Predicting the click-through rate for rare/new ads[R]. Center for Search and Information Extraction Lab International Institute of Information Technology Hyderabad, INDIA, 2010.
[10] REGELSON M, FAIN D. Predicting click-through rate using keyword clusters[C]//Proceedings of the Second Workshop on Sponsored Search Auctions, 2006:9623.
[11] RENDLE S. Factorization machines[C]//IEEE International Conference on Data Mining. IEEE Computer Society, 2010:995-1000.
[12] WANG X, LI W, CUI Y, et al. Click-through rate estimation for rare events in online advertising[G]//HUA X S, MEI T, HANJALIC A. Online Multimedia Advertising:Techniques and Technologies. Hershey:IGI Global, 2010. doi:10.4018/978-1-60960-189-8.ch001.
[13] AGARWAL D, BRODER A Z, CHAKRABARTI D, et al. Estimating rates of rare events at multiple resolutions[C]//ACM SIGKDD International Conference on Knowledge Discovery and Data Mining-Kdd. ACM, 2007:16-25.
[14] AGARWAL D, CHEN B C, ELANGO P. Spatio-temporal models for estimating click-through rate[C]//International Conference on World Wide Web. ACM, 2009:21-30.
[15] SCHONLAU M. Boosted regression (boosting):An introductory tutorial and a stata plugin[J]. Stata Journal, 2005, 5(3):330-354.
[16] BURGES C J C. From ranknet to lambdarank to lambdamart:An overview[R]. Microsoft Research Technical Report, 2010.
[17] FANG Y, LIU J. A novel prior-based real-time click through rate prediction model[J]. International Journal of Machine Learning & Cybernetics, 2014, 5(6):887-895.
[18] FAIN D C, PEDERSEN J O. Sponsored search:A brief history[J]. Bulletin of the American Society for Information Science & Technology, 2010, 32(2):12-13.
[19] RICHARDSON M, DOMINOWSKA E, RAGNO R. Predicting clicks:estimating the click-through rate for new ads[C]//International Conference on World Wide Web. ACM, 2007:521-530.
[20] JOACHIMS T, GRANKA L, PAN B, et al. Accurately interpreting clickthrough data as implicit feedback[C]//Proceedings of the 28th Annual International ACM SIGIR, 2005:154-161.
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