Sampling RTB transactions in an online machine learning setting
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 est...
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Formato: | Objeto de conferencia |
Lenguaje: | Inglés |
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2016
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Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/56845 http://45jaiio.sadio.org.ar/sites/default/files/AGRANDA-11.pdf |
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I19-R120-10915-56845 |
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Universidad Nacional de La Plata |
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I-19 |
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R-120 |
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SEDICI (UNLP) |
language |
Inglés |
topic |
Ciencias Informáticas Streaming events demand-side platform events |
spellingShingle |
Ciencias Informáticas Streaming events demand-side platform events Pita, Carlos Sampling RTB transactions in an online machine learning setting |
topic_facet |
Ciencias Informáticas Streaming events demand-side platform events |
description |
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. |
format |
Objeto de conferencia Objeto de conferencia |
author |
Pita, Carlos |
author_facet |
Pita, Carlos |
author_sort |
Pita, Carlos |
title |
Sampling RTB transactions in an online machine learning setting |
title_short |
Sampling RTB transactions in an online machine learning setting |
title_full |
Sampling RTB transactions in an online machine learning setting |
title_fullStr |
Sampling RTB transactions in an online machine learning setting |
title_full_unstemmed |
Sampling RTB transactions in an online machine learning setting |
title_sort |
sampling rtb transactions in an online machine learning setting |
publishDate |
2016 |
url |
http://sedici.unlp.edu.ar/handle/10915/56845 http://45jaiio.sadio.org.ar/sites/default/files/AGRANDA-11.pdf |
work_keys_str_mv |
AT pitacarlos samplingrtbtransactionsinanonlinemachinelearningsetting |
bdutipo_str |
Repositorios |
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1764820477685006336 |