Feature-based approaches for ethical news personalisation

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Journal Title

Journal ISSN

Volume Title

Perustieteiden korkeakoulu | Master's thesis

Authors

Date

2022-08-22

Department

Major/Subject

Data Science

Mcode

SCI3115

Degree programme

Master's Programme in ICT Innovation

Language

en

Pages

59

Series

Abstract

In recent years, the automation and optimization of content personalisation has become widespread in online mediums. Such recommendation approaches — recommender systems — create increased value to businesses through optimizing business objectives, while providing more accurate suggestions to users, increasing customer satisfaction. In contrast to usual approaches, when considering the automation of news personalisation, ethical journalistic responsibilities have to be taken into account alongside business and user objectives. This thesis evaluates an industry recommender system of a Finnish Media company and describes a novel interpretable model for improving its performance. These improvements are evaluated theoretically and experimentally, showing the marked increase in performance through leveraging previously unused latent information.

Description

Supervisor

Jung, Alexander

Thesis advisor

Westrup, Clemens

Keywords

recommender systems, CTR, interpretable ML, regression-based latent factor models, feature-based methods

Other note

Citation

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-202209045289

Collections

[dipl] Perustieteiden korkeakoulu / SCI