ABSTRACT
Online advertising allows advertisers to only bid and pay for measurable user responses, such as clicks on ads. As a consequence, click prediction systems are central to most online advertising systems. With over 750 million daily active users and over 1 million active advertisers, predicting clicks on Facebook ads is a challenging machine learning task. In this paper we introduce a model which combines decision trees with logistic regression, outperforming either of these methods on its own by over 3%, an improvement with significant impact to the overall system performance. We then explore how a number of fundamental parameters impact the final prediction performance of our system. Not surprisingly, the most important thing is to have the right features: those capturing historical information about the user or ad dominate other types of features. Once we have the right features and the right model (decisions trees plus logistic regression), other factors play small roles (though even small improvements are important at scale). Picking the optimal handling for data freshness, learning rate schema and data sampling improve the model slightly, though much less than adding a high-value feature, or picking the right model to begin with.
- R. Ananthanarayanan, V. Basker, S. Das, A. Gupta, H. Jiang, T. Qiu, A. Reznichenko, D. Ryabkov, M. Singh, and S. Venkataraman. Photon: Fault-tolerant and scalable joining of continuous data streams. In SIGMOD, 2013. Google ScholarDigital Library
- L. Bottou. Online algorithms and stochastic approximations. In D. Saad, editor, Online Learning and Neural Networks. Cambridge University Press, Cambridge, UK, 1998. revised, oct 2012. Google ScholarDigital Library
- O. Chapelle and L. Li. An empirical evaluation of thompson sampling. In Advances in Neural Information Processing Systems, volume 24, 2012.Google Scholar
- B. Edelman, M. Ostrovsky, and M. Schwarz. Internet advertising and the generalized second price auction: Selling billions of dollars worth of keywords. In American Economic Review, volume 97, pages 242--259, 2007.Google Scholar
- J. H. Friedman. Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29:1189--1232, 1999.Google ScholarCross Ref
- L. Golab and M. T. Özsu. Processing sliding window multi-joins in continuous queries over data streams. In VLDB, pages 500--511, 2003. Google ScholarDigital Library
- T. Graepel, J. Quiñonero Candela, T. Borchert, and R. Herbrich. Web-scale bayesian click-through rate prediction for sponsored search advertising in Microsoft's Bing search engine. In ICML, pages 13--20, 2010.Google ScholarDigital Library
- H. B. McMahan, G. Holt, D. Sculley, M. Young, D. Ebner, J. Grady, L. Nie, T. Phillips, E. Davydov, D. Golovin, S. Chikkerur, D. Liu, M. Wattenberg, A. M. Hrafnkelsson, T. Boulos, and J. Kubica. Ad click prediction: a view from the trenches. In KDD, 2013. Google ScholarDigital Library
- M. Richardson, E. Dominowska, and R. Ragno. Predicting clicks: Estimating the click-through rate for new ads. In WWW, pages 521--530, 2007. Google ScholarDigital Library
- A. Thusoo, S. Antony, N. Jain, R. Murthy, Z. Shao, D. Borthakur, J. Sarma, and H. Liu. Data warehousing and analytics infrastructure at facebook. In SIGMOD, pages 1013--1020, 2010. Google ScholarDigital Library
- H. R. Varian. Position auctions. In International Journal of Industrial Organization, volume 25, pages 1163--1178, 2007.Google Scholar
- J. Yi, Y. Chen, J. Li, S. Sett, and T. W. Yan. Predictive model performance: Offline and online evaluations. In KDD, pages 1294--1302, 2013. Google ScholarDigital Library
Index Terms
- Practical Lessons from Predicting Clicks on Ads at Facebook
Recommendations
Predicting clicks: estimating the click-through rate for new ads
WWW '07: Proceedings of the 16th international conference on World Wide WebSearch engine advertising has become a significant element of the Web browsing experience. Choosing the right ads for the query and the order in which they are displayed greatly affects the probability that a user will see and click on each ad. This ...
Ads by whom? ads about what?: exploring user influence and contents in social advertising
COSN '13: Proceedings of the first ACM conference on Online social networksDespite the growing interest in using online social networking services (OSNS) for advertising, little is understood about what contributes to the social advertising performance. In this research, we pose following questions: How many clicks do social ...
Comments