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Genie: An Open Box Counterfactual Policy Estimator for Optimizing Sponsored Search Marketplace

Published: 30 January 2019 Publication History

Abstract

In this paper, we propose an offline counterfactual policy estimation framework called Genie to optimize Sponsored Search Marketplace. Genie employs an open box simulation engine with click calibration model to compute the KPI impact of any modification to the system. From the experimental results on Bing traffic, we showed that Genie performs better than existing observational approaches that employs randomized experiments for traffic slices that have frequent policy updates. We also show that Genie can be used to tune completely new policies efficiently without creating risky randomized experiments due to cold start problem. As time of today, Genie hosts more than $10000$ optimization jobs yearly which runs more than $30$ Million processing node hours of big data jobs for Bing Ads. For the last 3 years, Genie has been proven to be the one of the major platforms to optimize Bing Ads Marketplace due to its reliability under frequent policy changes and its efficiency to minimize risks in real experiments.

References

[1]
Susan Athey. 2015. Machine learning and causal inference for policy evaluation. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, 5--6.
[2]
Léon Bottou, Jonas Peters, Joaquin Qui nonero-Candela, Denis X Charles, D Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Simard, and Ed Snelson. 2013. Counterfactual reasoning and learning systems: The example of computational advertising. The Journal of Machine Learning Research, Vol. 14, 1 (2013), 3207--3260.
[3]
Andrei Broder, Peter Ciccolo, Evgeniy Gabrilovich, Vanja Josifovski, Donald Metzler, Lance Riedel, and Jeffrey Yuan. 2009. Online expansion of rare queries for sponsored search. In Proceedings of the 18th international conference on World wide web. ACM, 511--520.
[4]
Ronnie Chaiken, Bob Jenkins, Per-Åke Larson, Bill Ramsey, Darren Shakib, Simon Weaver, and Jingren Zhou. 2008. SCOPE: easy and efficient parallel processing of massive data sets. Proceedings of the VLDB Endowment, Vol. 1, 2 (2008), 1265--1276.
[5]
Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. ACM, 785--794.
[6]
Daniel C Fain and Jan O Pedersen. 2006. Sponsored search: A brief history. Bulletin of the Association for Information Science and Technology, Vol. 32, 2 (2006), 12--13.
[7]
Jerome H Friedman. 2001. Greedy function approximation: a gradient boosting machine. Annals of statistics (2001), 1189--1232.
[8]
Peter W Glynn and Donald L Iglehart. 1989. Importance sampling for stochastic simulations. Management Science, Vol. 35, 11 (1989), 1367--1392.
[9]
Gagan Goel, Vahab Mirrokni, and Renato Paes Leme. 2015. Polyhedral clinching auctions and the adwords polytope. Journal of the ACM (JACM), Vol. 62, 3 (2015), 18.
[10]
Avi Goldfarb. 2014. What is different about online advertising? Review of Industrial Organization, Vol. 44, 2 (2014), 115--129.
[11]
Thore Graepel, Joaquin Q Candela, Thomas Borchert, and Ralf Herbrich. 2010. Web-scale bayesian click-through rate prediction for sponsored search advertising in microsoft's bing search engine. In Proceedings of the 27th international conference on machine learning (ICML-10). 13--20.
[12]
Keisuke Hirano and Guido W Imbens. 2001. Estimation of causal effects using propensity score weighting: An application to data on right heart catheterization. Health Services and Outcomes research methodology, Vol. 2, 3--4 (2001), 259--278.
[13]
Ron Kohavi, Alex Deng, Brian Frasca, Toby Walker, Ya Xu, and Nils Pohlmann. 2013. Online controlled experiments at large scale. In The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, Chicago, IL, USA, August 11--14, 2013. 1168--1176.
[14]
Ron Kohavi, Randal M Henne, and Dan Sommerfield. 2007. Practical guide to controlled experiments on the web: listen to your customers not to the hippo. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 959--967.
[15]
Ron Kohavi, Roger Longbotham, Dan Sommerfield, and Randal M Henne. 2009. Controlled experiments on the web: survey and practical guide. Data mining and knowledge discovery, Vol. 18, 1 (2009), 140--181.
[16]
Lihong Li, Shunbao Chen, Jim Kleban, and Ankur Gupta. 2015. Counterfactual estimation and optimization of click metrics in search engines: A case study. In Proceedings of the 24th International Conference on World Wide Web. ACM, 929--934.
[17]
H Brendan McMahan, Gary Holt, David Sculley, Michael Young, Dietmar Ebner, Julian Grady, Lan Nie, Todd Phillips, Eugene Davydov, Daniel Golovin, et almbox. 2013. Ad click prediction: a view from the trenches. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1222--1230.
[18]
Matthew Richardson, Ewa Dominowska, and Robert Ragno. 2007. Predicting clicks: estimating the click-through rate for new ads. In Proceedings of the 16th international conference on World Wide Web. ACM, 521--530.
[19]
Nir Rosenfeld, Yishay Mansour, and Elad Yom-Tov. 2017. Predicting Counterfactuals from Large Historical Data and Small Randomized Trials. In Proceedings of the 26th International Conference on World Wide Web Companion, Perth, Australia, April 3--7, 2017. 602--609.
[20]
Amin Sayedi, Kinshuk Jerath, and Kannan Srinivasan. 2014. Competitive poaching in sponsored search advertising and its strategic impact on traditional advertising. Marketing Science, Vol. 33, 4 (2014), 586--608.
[21]
Tobias Schnabel, Paul N. Bennett, Susan T. Dumais, and Thorsten Joachims. 2018. Short-Term Satisfaction and Long-Term Coverage: Understanding How Users Tolerate Algorithmic Exploration. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, WSDM 2018, Marina Del Rey, CA, USA, February 5--9, 2018. 513--521.
[22]
Adith Swaminathan and Thorsten Joachims. 2015a. Batch learning from logged bandit feedback through counterfactual risk minimization. Journal of Machine Learning Research, Vol. 16 (2015), 1731--1755. http://dl.acm.org/citation.cfm?id=2886805
[23]
Adith Swaminathan and Thorsten Joachims. 2015b. The self-normalized estimator for counterfactual learning. In Advances in Neural Information Processing Systems. 3231--3239.
[24]
Hal R Varian. 2007. Position auctions. international Journal of industrial Organization, Vol. 25, 6 (2007), 1163--1178.
[25]
Hal R Varian and Christopher Harris. 2014. The VCG auction in theory and practice. American Economic Review, Vol. 104, 5 (2014), 442--45.
[26]
Wanhong Xu, Eren Manavoglu, and Erick Cantú -Paz. 2010. Temporal click model for sponsored search. In Proceeding of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, Geneva, Switzerland, July 19--23, 2010. 106--113.
[27]
Weinan Zhang, Shuai Yuan, and Jun Wang. 2014. Optimal real-time bidding for display advertising. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1077--1086.

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        cover image ACM Conferences
        WSDM '19: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining
        January 2019
        874 pages
        ISBN:9781450359405
        DOI:10.1145/3289600
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        Published: 30 January 2019

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        Author Tags

        1. causal inference
        2. counterfactual policy estimation
        3. sponsored search

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        WSDM '19 Paper Acceptance Rate 84 of 511 submissions, 16%;
        Overall Acceptance Rate 498 of 2,863 submissions, 17%

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        • (2023)Position Auctions for Sponsored Search in MarketplacesAdvances in Optimization and Applications10.1007/978-3-031-48751-4_10(131-144)Online publication date: 14-Dec-2023
        • (2022)Causal Inference in the Presence of Interference in Sponsored Search AdvertisingFrontiers in Big Data10.3389/fdata.2022.8885925Online publication date: 21-Jun-2022
        • (2022)Topological ForestIEEE Access10.1109/ACCESS.2022.322900810(131711-131721)Online publication date: 2022
        • (2021)ОЦІНКА ЕФЕКТИВНОСТІ ПРОСУВАННЯ ПРОДУКТУ НА ВЕЛИКИХ ТОРГОВИХ МАЙДАНЧИКАХЕкономіка та суспільство10.32782/2524-0072/2021-29-2Online publication date: 27-Jul-2021
        • (2021)Bid Prediction in Repeated Auctions with LearningProceedings of the Web Conference 202110.1145/3442381.3449968(3953-3964)Online publication date: 19-Apr-2021
        • (2021)Causal Transfer Random Forest: Combining Logged Data and Randomized Experiments for Robust PredictionProceedings of the 14th ACM International Conference on Web Search and Data Mining10.1145/3437963.3441722(211-219)Online publication date: 8-Mar-2021

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