{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T12:06:35Z","timestamp":1759147595583,"version":"3.41.0"},"reference-count":57,"publisher":"Association for Computing Machinery (ACM)","issue":"2-3","license":[{"start":{"date-parts":[[2017,7,3]],"date-time":"2017-07-03T00:00:00Z","timestamp":1499040000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Manage. Inf. Syst."],"published-print":{"date-parts":[[2017,9,30]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.1145\/3086188","type":"journal-article","created":{"date-parts":[[2017,7,5]],"date-time":"2017-07-05T12:19:53Z","timestamp":1499257193000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Apriori Rule--Based In-App Ad Selection Online Algorithm for Improving Supply-Side Platform Revenues"],"prefix":"10.1145","volume":"8","author":[{"given":"Anik","family":"Mukherjee","sequence":"first","affiliation":[{"name":"Indian Institute of Technology, Madras, Chennai, Tamil Nadu India"}]},{"given":"R. P.","family":"Sundarraj","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology, Madras, Chennai, Tamil Nadu India"}]},{"given":"Kaushik","family":"Dutta","sequence":"additional","affiliation":[{"name":"University of South Florida, E. Fowler Avenue, Tampa, Florida"}]}],"member":"320","published-online":{"date-parts":[[2017,7,3]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1002\/jos.74"},{"volume-title":"Retrieved","year":"2014","key":"e_1_2_1_2_1","unstructured":"Aerserv. 2014 . What Are Mobile Device Identifiers ? Retrieved May 19, 2017 from https:\/\/www.aerserv.com\/mobile-device-identifiers\/; retrieved March 12, 2015. Aerserv. 2014. What Are Mobile Device Identifiers? Retrieved May 19, 2017 from https:\/\/www.aerserv.com\/mobile-device-identifiers\/; retrieved March 12, 2015."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCA.2005.855918"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/945846.945848"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02430363"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2600057.2602874"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/128903.128905"},{"key":"e_1_2_1_8_1","unstructured":"M. Bijvank A. Haensel P. L\u2019Ecuyer and P. Marcotte. Time Dependent Bid Prices for Multi-Period Network Revenue Management Problems.  M. Bijvank A. Haensel P. L\u2019Ecuyer and P. Marcotte. Time Dependent Bid Prices for Multi-Period Network Revenue Management Problems."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/1242572.1242644"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.3115\/1075527.1075553"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/1250910.1250949"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1287\/moor.4.3.233"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1287\/ijoc.12.1.24.11899"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/SOCA.2011.6166216"},{"key":"e_1_2_1_15_1","volume-title":"Retrieved","author":"Datta A.","year":"2013","unstructured":"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. 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."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2011.11.022"},{"volume-title":"Retrieved","year":"2015","key":"e_1_2_1_17_1","unstructured":"DoubleClick. 2015 . DoubleClick home page . Retrieved May 19, 2017 from https:\/\/www.google.com.sg\/doubleclick\/). DoubleClick. 2015. DoubleClick home page. Retrieved May 19, 2017 from https:\/\/www.google.com.sg\/doubleclick\/)."},{"volume-title":"Retrieved","year":"2015","key":"e_1_2_1_18_1","unstructured":"Facebook. 2015 . Ad Delivery and Pacing Algorithms . Retrieved May 19, 2017 from https:\/\/developers.facebook.com\/docs\/marketing-api\/pacing. Facebook. 2015. Ad Delivery and Pacing Algorithms. Retrieved May 19, 2017 from https:\/\/developers.facebook.com\/docs\/marketing-api\/pacing."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/38714.38735"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2005.11.008"},{"volume-title":"Retrieved","year":"2017","key":"e_1_2_1_21_1","unstructured":"Google. AdWords homepage. 2015 . Retrieved May 19, 2017 from https:\/\/www.google.com.sg\/adwords\/. Google. AdWords homepage. 2015. Retrieved May 19, 2017 from https:\/\/www.google.com.sg\/adwords\/."},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1287\/ijoc.2.1.94"},{"key":"e_1_2_1_23_1","unstructured":"M. Hahsler B. Grun K. Hornik and C. Buchta. 2007. Introduction to arules--a computational environment for mining association rules and frequent item sets.  M. Hahsler B. Grun K. Hornik and C. Buchta. 2007. Introduction to arules--a computational environment for mining association rules and frequent item sets."},{"key":"e_1_2_1_24_1","volume-title":"and D. Greene","author":"Hermann J.","year":"2005","unstructured":"J. Hermann , C. Francine , and F. Ayman , and D. Greene . 2005 a. Keyword advertisement management. J. Hermann, C. Francine, and F. Ayman, and D. Greene. 2005a. Keyword advertisement management."},{"volume-title":"Patent No: US20050137939 A1.","author":"Hermann J.","key":"e_1_2_1_25_1","unstructured":"J. Hermann , C. Francine , and F. Ayman . and D. Greene. 2005b. Server-based keyword advertisement management . Patent No: US20050137939 A1. J. Hermann, C. Francine, and F. Ayman. and D. Greene. 2005b. Server-based keyword advertisement management. Patent No: US20050137939 A1."},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.2307\/25148625"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1287\/opre.1060.0371"},{"key":"e_1_2_1_28_1","doi-asserted-by":"crossref","unstructured":"J. N. Hooker. 2006. Operations research methods in constraint programming. Handbook of Constraint Programming.  J. N. Hooker. 2006. Operations research methods in constraint programming. Handbook of Constraint Programming.","DOI":"10.1016\/S1574-6526(06)80019-2"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02430364"},{"key":"e_1_2_1_30_1","doi-asserted-by":"crossref","unstructured":"J. N. Hooker. 1994a. Logic-based methods for optimization. Principles and Practice of Constraint Programming. 336--349.   J. N. Hooker. 1994a. Logic-based methods for optimization. Principles and Practice of Constraint Programming. 336--349.","DOI":"10.1007\/3-540-58601-6_111"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1287\/opre.42.2.201"},{"key":"e_1_2_1_32_1","volume-title":"Retrieved","author":"IAB.","year":"2014","unstructured":"IAB. 2014 . IAB\u2014IAB Internet Advertising Revenue Report . Retrieved May 19, 2017 from http:\/\/www.iab.net\/research\/industry_data_and_landscape\/adrevenuereport. IAB. 2014. IAB\u2014IAB Internet Advertising Revenue Report. Retrieved May 19, 2017 from http:\/\/www.iab.net\/research\/industry_data_and_landscape\/adrevenuereport."},{"key":"e_1_2_1_33_1","volume-title":"Retrieved","author":"IABRTB.","year":"2014","unstructured":"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. 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."},{"key":"e_1_2_1_34_1","unstructured":"IAB. 2016. http:\/\/www.iab.com\/wpcontent\/uploads\/2016\/04\/IAB_Internet_Advertising_Revenue_Report_HY_2016.pdf.  IAB. 2016. http:\/\/www.iab.com\/wpcontent\/uploads\/2016\/04\/IAB_Internet_Advertising_Revenue_Report_HY_2016.pdf."},{"key":"e_1_2_1_35_1","volume-title":"New York: Springer.","author":"James G.","year":"2013","unstructured":"G. James , D. Witten , T. Hastie , and R. Tibshirani . 2013 . An introduction to statistical learning. New York: Springer. G. James, D. Witten, T. Hastie, and R. Tibshirani. 2013. An introduction to statistical learning. New York: Springer."},{"key":"e_1_2_1_36_1","volume-title":"Retrieved","author":"Johnson D. S.","year":"2001","unstructured":"D. S. Johnson . 2001 . A Theoretician's Guide to the Experimental Analysis of Algorithms . Retrieved May 19, 2017 from http:\/\/www.cc.gatech.edu\/&sim;bader\/COURSES\/GATECH\/CSE-Algs-Fall2013\/papers\/Joh01.pdf. D. S. Johnson. 2001. A Theoretician's Guide to the Experimental Analysis of Algorithms. Retrieved May 19, 2017 from http:\/\/www.cc.gatech.edu\/&sim;bader\/COURSES\/GATECH\/CSE-Algs-Fall2013\/papers\/Joh01.pdf."},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1080\/1019678042000245119"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2007.190640"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2005.07.005"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339651"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/2883816"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1287\/ijoc.8.1.1"},{"key":"e_1_2_1_43_1","unstructured":"B. D. Marsh and J. D. McAuliffe. 1998. Method and apparatus for scheduling the presentation of messages to computer users. Patent No: US005848397A.  B. D. Marsh and J. D. McAuliffe. 1998. Method and apparatus for scheduling the presentation of messages to computer users. Patent No: US005848397A."},{"key":"e_1_2_1_44_1","volume-title":"Retrieved","author":"Marvin G.","year":"2014","unstructured":"G. Marvin . 2014 . U. S. Programmatic ad spend to top &dollar;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. G. Marvin. 2014. U. S. Programmatic ad spend to top &dollar;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."},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1287\/ijoc.1020.0003"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1287\/opre.2016.1553"},{"volume-title":"Proceedings of the 25th International Conference on Workshop on Information Technology and Systems (WITS\u201915)","author":"Mukherjee A.","key":"e_1_2_1_47_1","unstructured":"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\u201915) , Dallas, TX. 1--17. 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\u201915), Dallas, TX. 1--17."},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1287\/msom.2014.0491"},{"volume-title":"So Much Time. Retrieved","year":"2014","key":"e_1_2_1_49_1","unstructured":"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. 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."},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.2753\/MIS0742-1222240302"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/141484.130294"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.2015.2185"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/1242572.1242643"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1057\/rpm.2009.10"},{"key":"e_1_2_1_55_1","unstructured":"K. Srinivasan and M. I. Shamos. 2010. Determining the effectiveness of Internet advertising. United States Patent No: US 7 747 465 B2.  K. Srinivasan and M. I. Shamos. 2010. Determining the effectiveness of Internet advertising. United States Patent No: US 7 747 465 B2."},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1287\/opre.1110.0996"},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1287\/opre.1100.0852"}],"container-title":["ACM Transactions on Management Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3086188","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3086188","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T03:37:01Z","timestamp":1750217821000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3086188"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,7,3]]},"references-count":57,"journal-issue":{"issue":"2-3","published-print":{"date-parts":[[2017,9,30]]}},"alternative-id":["10.1145\/3086188"],"URL":"https:\/\/doi.org\/10.1145\/3086188","relation":{},"ISSN":["2158-656X","2158-6578"],"issn-type":[{"type":"print","value":"2158-656X"},{"type":"electronic","value":"2158-6578"}],"subject":[],"published":{"date-parts":[[2017,7,3]]},"assertion":[{"value":"2016-06-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2017-04-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2017-07-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}