Abstract
Today, smartphone-based in-app advertisement forms a substantial portion of the online advertising market. In-app publishers go through ad-space aggregators known as Supply-Side Platforms (SSPs), who, in turn, act as intermediaries for ad-agency aggregators known as demand-side platforms. The SSPs face the twin issue of making ad placement decisions within an order of milliseconds, even though their revenue streams can be optimized only by a careful selection of ads that elicit appropriate user responses regarding impressions, clicks, and conversions. This article considers the SSP's perspective and presents an online algorithm that balances these two issues. Our experimental results indicate that the decision-making time generally ranges between 20 ms and 50 ms and accuracy from 1% to 10%. Further, we conduct statistical analysis comparing the theoretical complexity of the online algorithm with its empirical performance. Empirically, we observe that the time is directly proportional to the number of incoming ads and the number of online rules.
- M. Adler, P. B. Gibbons, and Y. Matias. 2002. Scheduling space-sharing for Internet advertising. Journal of Scheduling. 5, 2, 103--119.Google ScholarCross Ref
- Aerserv. 2014. What Are Mobile Device Identifiers? Retrieved May 19, 2017 from https://www.aerserv.com/mobile-device-identifiers/; retrieved March 12, 2015.Google Scholar
- A. Amiri and S. Menon. 2006. Scheduling web banner advertisements with multiple display frequencies. IEEE Transactions on Systems. 36, 2, 245--251. Google ScholarDigital Library
- A. Amiri and S. Menon. 2003. Efficient scheduling of Internet banner advertisements. ACM Transactions on Internet Technology. 3, 4, 334--346. Google ScholarDigital Library
- R. S. Barr, B. L. Golden, J. P. Kelly, M. G. C. Resende, and W. R. Stewart. 1995. Designing and reporting on computational experiments with heuristic methods. Journal of Heuristics. 1, 1, 9--32.Google ScholarDigital Library
- M. Bateni, J. Feldman, V. Mirrokni, and C. S. Wong. 2014. Multiplicative bidding in online advertising, In Proceedings of EC’14. Google ScholarDigital Library
- L. Becker and H. G. Ralf. 1992. Rule-based optimization and query processing in an extensible geometric database system. ACM Transactions on Database Systems (TODS). 17, 2, 247--303. Google ScholarDigital Library
- M. Bijvank, A. Haensel, P. L’Ecuyer, and P. Marcotte. Time Dependent Bid Prices for Multi-Period Network Revenue Management Problems.Google Scholar
- C. Borgs, J. Chayes, N. Immorlica, K. Jain, O. Etesami, and M. Mahdian. 2007. Dynamics of bid optimization in online advertisement auctions. In 16th International Conference on the World Wide Web - WWW’07. 531--540. Google ScholarDigital Library
- E. A. Brill. 1992. Simple rule-based part of speech tagger. In Workshop on Speech and Natural Language. Association for Computational Linguistics. Google ScholarDigital Library
- M. Cary, A. Das, B. Edelman, I. Giotis, K. Heimerl, A. R. Karlin, C. Matheiu, and M. Schwarz. 2007. Greedy bidding strategies for keyword auctions. In 8th ACM Conference on Electronic Commerce (EC’07), 262--271. Google ScholarDigital Library
- V. Chvatal. 1979. A greedy heuristic for the set-covering problem. Mathematics of Operations Research. 4, 3, 233--235. Google ScholarDigital Library
- M. Coffin and M. J. Saltzman. 2000. Statistical analysis of computational tests of algorithms and heuristics. INFORMS Journal on Computing. 12, 1, 24--44.Google ScholarCross Ref
- T. Cucinotta and G. T. A. Anastasi. 2011. Heuristic for optimum allocation of real-time service workflows. 2011. In IEEE International Conference on Service-Oriented Computing and Applications. Google ScholarDigital Library
- A. Datta. 2013. Why Is the success of mobile apps so difficult to measure? Critical issue in audience measurement unravelled. Retrieved May 19, 2017 from http://www.huffingtonpost.com/anindya-datta/success-of-mobile-apps_b_2860915.html.Google Scholar
- J. Deane. 2012. Hybrid genetic algorithm and augmented neural network application for solving the online advertisement scheduling problem with contextual targeting. Expert Systems with Applications 39, 5, 5168--5177. Google ScholarDigital Library
- DoubleClick. 2015. DoubleClick home page. Retrieved May 19, 2017 from https://www.google.com.sg/doubleclick/).Google Scholar
- Facebook. 2015. Ad Delivery and Pacing Algorithms. Retrieved May 19, 2017 from https://developers.facebook.com/docs/marketing-api/pacing.Google Scholar
- J. C. Freytag. 1987. A rule-based view of query optimization. ACM. 16, 3. Google ScholarDigital Library
- K. Gnanendran and R. P. Sundarraj. 2006. Alternative model representations and computing capacity: Implications for model management. Decision Support Systems. 42, 3, 1413--1430. Google ScholarDigital Library
- Google. AdWords homepage. 2015. Retrieved May 19, 2017 from https://www.google.com.sg/adwords/.Google Scholar
- H. J. Greenberg. 1990. Computational testing: Why, how and how much. ORSA Journal on Computing. 2, 1, 94--97.Google ScholarCross Ref
- M. Hahsler, B. Grun, K. Hornik, and C. Buchta. 2007. Introduction to arules--a computational environment for mining association rules and frequent item sets.Google Scholar
- J. Hermann, C. Francine, and F. Ayman, and D. Greene. 2005a. Keyword advertisement management.Google Scholar
- J. Hermann, C. Francine, and F. Ayman. and D. Greene. 2005b. Server-based keyword advertisement management. Patent No: US20050137939 A1.Google Scholar
- A. R. Hevner, S. T. March, J. Park, and S. Ram. 2004. Design science in information systems research. MIS Quarterly. 28, 1, 75--105. Google ScholarCross Ref
- J. N. Hooker. 2007. Planning and scheduling by logic-based benders decomposition. Operations Research. 55, 3, 588--602. Google ScholarDigital Library
- J. N. Hooker. 2006. Operations research methods in constraint programming. Handbook of Constraint Programming.Google Scholar
- J. N. Hooker. 1995. Testing heuristics: We have it all wrong. Journal of Heuristics. 1, 1, 33--42.Google ScholarDigital Library
- J. N. Hooker. 1994a. Logic-based methods for optimization. Principles and Practice of Constraint Programming. 336--349. Google ScholarDigital Library
- J. N. Hooker. 1994b. Needed: An empirical science of algorithms. Operations Research. 42, 2, 201--212. Google ScholarDigital Library
- IAB. 2014. IAB—IAB Internet Advertising Revenue Report. Retrieved May 19, 2017 from http://www.iab.net/research/industry_data_and_landscape/adrevenuereport.Google Scholar
- IABRTB. 2014. OpenRTB API Specification Version 2.0. Retrieved May 19, 2017 from http://www.iab.net/media/file/OpenRTB_API_Specification_Version2.0_FINAL.PDF.Google Scholar
- IAB. 2016. http://www.iab.com/wpcontent/uploads/2016/04/IAB_Internet_Advertising_Revenue_Report_HY_2016.pdf.Google Scholar
- G. James, D. Witten, T. Hastie, and R. Tibshirani. 2013. An introduction to statistical learning. New York: Springer. Google ScholarDigital Library
- D. S. Johnson. 2001. A Theoretician's Guide to the Experimental Analysis of Algorithms. Retrieved May 19, 2017 from http://www.cc.gatech.edu/∼bader/COURSES/GATECH/CSE-Algs-Fall2013/papers/Joh01.pdf.Google Scholar
- B. Kitts and B. Leblanc. 2004. Optimal bidding on keyword auctions. Electronic Markets. 14, 3, 186--201.Google ScholarCross Ref
- S. Kumar, M. Dawande, and V. S. Mookerjee. 2007. Optimal scheduling and placement of Internet banner advertisement. IEEE Transactions on Knowledge and Data Engineering. 19, 11, 1571--1584 Google ScholarDigital Library
- S. Kumar, V. S. Jacob, and C. Sriskandarajah. 2006. Scheduling advertisements on a web page to maximize revenue. European Journal of Operational Research. 173, 3, 1067--1089.Google ScholarCross Ref
- K. Lee, B. Orten, A. Dasdan, and W. Li, 2012. Estimating conversion rate in display advertising from past performance data. In Proceedings of KDD’12. Google ScholarDigital Library
- X. Lu, X. Zhao, and L. Xue. 2016. Is combining contextual and behavioral targeting strategies effective in online advertising? ACM Transactions on Management Information Systems. 7, 1, 1--20. Google ScholarDigital Library
- C. C. McGeoch. 1996. Feature article—toward an experimental method for algorithm simulation. INFORMS Journal on Computing 8(1):1--15. Google ScholarDigital Library
- B. D. Marsh and J. D. McAuliffe. 1998. Method and apparatus for scheduling the presentation of messages to computer users. Patent No: US005848397A.Google Scholar
- G. Marvin. 2014. U. S. Programmatic ad spend to top $10 billion in 2014 and double by 2016, emarketer. Retrieved May 19, 2017 from http://marketingland.com/us-programmatic-ad-spend-10-billion-2014-double-2016-emarketer-104112.Google Scholar
- S. Menon and A. Amiri. 2004. Scheduling banner advertisements on the web. INFORMS Journal on Computing. 16, 1, 95--105. Google ScholarDigital Library
- R. Mookerjee, S. Kumar, and V. S. Mookerjee. 2016. Optimizing performance-based Internet advertisement campaigns. Operations Research. 65, 1, 38--54.Google ScholarDigital Library
- A. Mukherjee, R. P. Sundarraj, and K. Dutta. 2015. Online programmatic ad-placement for supply side platform of mobile advertisement: An apriori rule-generation approach. In Proceedings of the 25th International Conference on Workshop on Information Technology and Systems (WITS’15), Dallas, TX. 1--17.Google Scholar
- S. Najafi-Asadolahi and K. Fridgeirsdottir. 2014. Cost-per-click pricing for display advertising. Manufacturing Service Operation Management. 16, 4, 482--497. Google ScholarDigital Library
- Nielsen. 2014. Smartphones: So Many Apps, So Much Time. Retrieved May 19, 2017 from http://www.nielsen.com/us/en/insights/news/2014/smartphones-so-many-apps--so-much-time.html.Google Scholar
- K. Peffers, T. Tuunanen, A. M. Rothenberger, and S. Chatterjee. 2007. A design science research methodology for information systems research. Journal of Management Information Systems. 24, 3, 45--77. Google ScholarDigital Library
- H. Pirahesh, M. H. Joseph, and H. Waqar. 1992. Extensible/rule based query rewrite optimization in Starburst. ACM Sigmod Record. 21, 2. Google ScholarDigital Library
- G. D. Popescu and P. Crama. 2016. Ad revenue optimization in live broadcasting. Management Science. 62, 4, 1145--1164.Google ScholarCross Ref
- M. Richardson, E. Dominowska, and R. Rango. 2007. Predicting clicks: estimating the click-through rate for new ads. In Proceedings of WWW’07. Google ScholarDigital Library
- G. Roels and K. Fridgeirsdottir. 2009. Dynamic revenue management for online display advertising. Journal of Revenue Pricing Management. 8, 5, 452--466.Google ScholarCross Ref
- K. Srinivasan and M. I. Shamos. 2010. Determining the effectiveness of Internet advertising. United States Patent No: US 7,747,465 B2.Google Scholar
- J. Turner. 2012. The planning of guaranteed targeted display advertising. Operations Research 60, 1, 18--33. Google ScholarDigital Library
- J. Turner, A. Scheller-Wolf, and S. Tayur. 2011. Scheduling of dynamic in-game advertising. Operations Research 59, 1, 1--16. Google ScholarDigital Library
Index Terms
- Apriori Rule--Based In-App Ad Selection Online Algorithm for Improving Supply-Side Platform Revenues
Recommendations
Optimal real-time bidding for display advertising
KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data miningIn this paper we study bid optimisation for real-time bidding (RTB) based display advertising. RTB allows advertisers to bid on a display ad impression in real time when it is being generated. It goes beyond contextual advertising by motivating the ...
Profit Maximization for Online Advertising Demand-Side Platforms
ADKDD'17: Proceedings of the ADKDD'17We develop an optimization model and corresponding algorithm for the management of a demand-side platform (DSP), whereby the DSP aims to maximize its own profit while acquiring valuable impressions for its advertiser clients. We formulate the problem of ...
A New Approach to Real-Time Bidding in Online Advertisements: Auto Pricing Strategy
Real-time bidding (RTB) for digital advertising is becoming the norm for improving advertisers’ campaigns. Unlike traditional advertising practices, in the process of RTB, the advertisement slots of a mobile application or a website are mapped to a ...
Comments