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Abstract
The focus in this thesis is investigation of machine learning methods with applications in computational advertising. Computational advertising is the broad discipline of building systems which can reach audiences browsing the Internet with targeted advertisements. At the core of such systems, algorithms are needed for making decisions. It is in one such particular instance of computational advertising, namely in web banner advertising, that we investigate machine learning methods to assist and make decisions in order to optimize the placements of ads.
The industrial partner in this work is Adform, an international online advertising technology partner. This also means that the analyses and methods in this work are developed with particular usecases within Adform in mind and thus need also to be applicable in Adform’s technology stack. This implies extra thought on scalability and performance.
The particular usecase which is used as a benchmark for our results, is clickthrough rate prediction. In this task one aims to predict the probability that a user will click on an advertisement, based on attributes about the user, the advertisement the context, and other signals, such as time. This has its main application in realtime bidding ad exchanges, where each advertiser is given a chance to place bids for showing their ad while the page loads, and the winning bid gets to display their banner.
The contributions of this thesis entail application of a hybrid model of explicit and latent features for learning probabilities of clicks, which is a methodological extension of the current model in production at Adform. Our findings confirm that latent features can increase predictive performance in the setup of clickthrough rate prediction. They also reveal a tedious process for tuning the model for optimal performance.
We also present variations of Bayesian generative models for stochastic blockmodeling for inference of structure based on browsing patterns. Applying this structural information to improve clickthrough rate prediction becomes a twostep procedure; 1) learn user and URL profiles from browsing patterns, 2) use the profiles as additional features in a clickthrough rate prediction model. The assumption we implicitly make is reasonable: Users and URLs that are grouped together based on browsing patterns will have similar responses to ads, e.g., can be used as predictors of clicks. We report successful examples of applying this approach in practice.
Finally, we introduce the multiplenetworks stochastic blockmodel (MNSBM), a model for efficient overlapping community detection in complex networks which can be assumed to be an aggregation of multiple blockstructured subnetworks.
The industrial partner in this work is Adform, an international online advertising technology partner. This also means that the analyses and methods in this work are developed with particular usecases within Adform in mind and thus need also to be applicable in Adform’s technology stack. This implies extra thought on scalability and performance.
The particular usecase which is used as a benchmark for our results, is clickthrough rate prediction. In this task one aims to predict the probability that a user will click on an advertisement, based on attributes about the user, the advertisement the context, and other signals, such as time. This has its main application in realtime bidding ad exchanges, where each advertiser is given a chance to place bids for showing their ad while the page loads, and the winning bid gets to display their banner.
The contributions of this thesis entail application of a hybrid model of explicit and latent features for learning probabilities of clicks, which is a methodological extension of the current model in production at Adform. Our findings confirm that latent features can increase predictive performance in the setup of clickthrough rate prediction. They also reveal a tedious process for tuning the model for optimal performance.
We also present variations of Bayesian generative models for stochastic blockmodeling for inference of structure based on browsing patterns. Applying this structural information to improve clickthrough rate prediction becomes a twostep procedure; 1) learn user and URL profiles from browsing patterns, 2) use the profiles as additional features in a clickthrough rate prediction model. The assumption we implicitly make is reasonable: Users and URLs that are grouped together based on browsing patterns will have similar responses to ads, e.g., can be used as predictors of clicks. We report successful examples of applying this approach in practice.
Finally, we introduce the multiplenetworks stochastic blockmodel (MNSBM), a model for efficient overlapping community detection in complex networks which can be assumed to be an aggregation of multiple blockstructured subnetworks.
Original language  English 

Place of Publication  Kgs. Lyngby 

Publisher  Technical University of Denmark 
Number of pages  129 
Publication status  Published  2015 
Series  DTU Compute PHD2014 

Number  355 
ISSN  09093192 
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 1 Finished

Statistical learning for predictive targeting in online advertising
Fruergaard, B. Ø., Hansen, L. K., Urban, J., Winther, O., Igel, C. & Candela, J.
01/12/2011 → 04/03/2015
Project: PhD