Sampling RTB transactions in an online machine learning setting

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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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Autor principal: Pita, Carlos
Formato: Articulo
Lenguaje:Inglés
Publicado: 2017
Materias:
Ciencias Informáticas
machine learning
real-time bidding
online advertising market
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/135156
https://publicaciones.sadio.org.ar/index.php/EJS/article/view/20
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-135156
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
machine learning
real-time bidding
online advertising market
spellingShingle Ciencias Informáticas
machine learning
real-time bidding
online advertising market
Pita, Carlos
Sampling RTB transactions in an online machine learning setting
topic_facet Ciencias Informáticas
machine learning
real-time bidding
online advertising market
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 Articulo
Articulo
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 2017
url http://sedici.unlp.edu.ar/handle/10915/135156
https://publicaciones.sadio.org.ar/index.php/EJS/article/view/20
work_keys_str_mv AT pitacarlos samplingrtbtransactionsinanonlinemachinelearningsetting
bdutipo_str Repositorios
_version_ 1764820456414642176

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