Feature-based Approaches for Ethical News Personalisation
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URL
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, AlexanderThesis advisor
Westrup, ClemensKeywords
recommender systems, CTR, interpretable ML, regression-based latent factor models, feature-based methods