Szczegóły publikacji

Opis bibliograficzny

Fast Click-Through Rate estimation using data aggregates / Roman WIATR, Renata G. SŁOTA, Jacek KITOWSKI // W: Computational Science – ICCS 2021 : 21st international conference : Krakow, Poland, June 16–18, 2021 : proceedings, Pt. 1 / eds. Maciej Paszyński, [et al.]. — Cham : Springer Nature Switzerland, cop. 2021. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 12742. Theoretical Computer Science and General Issues ; ISSN 0302-9743). — ISBN: 978-3-030-77960-3; e-ISBN: 978-3-030-77961-0. — S. 685–698. — Bibliogr., Abstr. — Publikacja dostępna online od: 2021-06-09. — J. Kitowski - dod. afiliacja: Academic Computer Centre CYFRONET AGH


Autorzy (3)


Słowa kluczowe

logistic regressionReal Time BiddingClick-Through RateCTRdimensionality reductionRTB

Dane bibliometryczne

ID BaDAP134704
Data dodania do BaDAP2021-06-23
DOI10.1007/978-3-030-77961-0_54
Rok publikacji2021
Typ publikacjimateriały konferencyjne (aut.)
Otwarty dostęptak
WydawcaSpringer
Konferencja21st International Conference on Computational Science
Czasopisma/serieLecture Notes in Computer Science, Theoretical Computer Science and General Issues

Abstract

Click-Through Rate estimation is a crucial prediction task in Real-Time Bidding environments prevalent in display advertising. The estimation provides information on how to trade user visits in various systems. Logistic Regression is a popular choice as the model for this task. Due to the amount, dimensionality and sparsity of data, it is challenging to train and evaluate the model. One of the techniques to reduce the training and evaluation cost is dimensionality reduction. In this work, we present Aggregate Encoding, a technique for dimensionality reduction using data aggregates. Our approach is to build aggregate-based estimators and use them as an ensemble of models weighted by logistic regression. The novelty of our work is the separation of feature values according to the value frequency, to better utilise regularization. For our experiments, we use the iPinYou data set, but this approach is universal and can be applied to other problems requiring dimensionality reduction of sparse categorical data.

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