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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 |