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dc.date.accessioned 2022-04-27T16:04:15Z
dc.date.available 2022-04-27T16:04:15Z
dc.date.issued 2017-06-04
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/135156
dc.description.abstract We (the machine learning team at Jampp) strive to predict click-through rates (CTR) and conversion rates (CVR) for the real-time bidding (RTB) online advertising market by means of an in-house online machine learning platform based on a state-of-the-art stochastic gradient descent estimator. Our estimation framework has already been covered in a previous paper, so here we want to focus on some peripheral aspects of our platform that, in spite of being of a somewhat ancillary nature, nevertheless tend to dominate development efforts and overall system complexity; namely, in order to feed the learning system we first need to sample a very high-volume stream of out-of-order and scattered-in-time events and consolidate them into a sequence of observations representing the underlying market transactions, each observation composed of a set of features and a response, from which the estimator is ultimately able to learn. This paper is written in a down-to-earth fashion: we describe a number of particular difficulties the general problem of sampling in an online high-volume setting poses and then we present our concrete answers to those difficulties based on real, hands-on, experience. en
dc.format.extent 46-53 es
dc.language en es
dc.subject machine learning es
dc.subject real-time bidding es
dc.subject online advertising market es
dc.title Sampling RTB transactions in an online machine learning setting en
dc.type Articulo es
sedici.identifier.uri https://publicaciones.sadio.org.ar/index.php/EJS/article/view/20 es
sedici.identifier.issn 1514-6774 es
sedici.creator.person Pita, Carlos es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Sociedad Argentina de Informática e Investigación Operativa es
sedici.subtype Articulo es
sedici.rights.license Creative Commons Attribution 4.0 International (CC BY 4.0)
sedici.rights.uri http://creativecommons.org/licenses/by/4.0/
sedici.description.peerReview peer-review es
sedici.relation.journalTitle Electronic Journal of SADIO es
sedici.relation.journalVolumeAndIssue vol. 16 es


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Creative Commons Attribution 4.0 International (CC BY 4.0) Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution 4.0 International (CC BY 4.0)