Predicting the click-through rate of ads using factorization machines

Robert Dovžan (2019) Predicting the click-through rate of ads using factorization machines. EngD thesis.

Preview

Abstract

In the field of programmatic advetising based on the ecosystem called realtime bidding, it is important to know, how successful an ad impression will be. Click-through rate prediction is one of the biggest challenges in online advertising. In this thesis we use factorization machines to predict the clickthrough rate based on data about the ad, website, user etc. We describe the process of data preparation, feature selection, implementation and testing. The goal is to improve the current solution in company Zemanta d.o.o. which is based on logistic regression. With local testing and online A/B testing we reach our goal and contribute to improving the service and financial performance of the company

Item Type:

Thesis (EngD thesis)

Keywords:

factorization machines, advertising, ads, prediction, machine learning, data mining

Number of Pages:

Language of Content:

Slovenian

Mentor / Comentors:

Name and Surname	ID	Function
doc. dr. Lovro Šubelj		Mentor
Davorin Kopič		Comentor

Link to COBISS:

http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1538205891)

Institution:

University of Ljubljana

Department:

Faculty of Computer and Information Science

Item ID:

4416

Date Deposited:

26 Mar 2019 11:54

Last Modified:

16 Apr 2019 12:37

URI:

http://eprints.fri.uni-lj.si/id/eprint/4416

Actions (login required)

View Item