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DOI

The growing volume of digital data stimulates the adoption of recommender systems in different socioeconomic domains, including news industries. While news recommenders help consumers deal with information overload and increase their engagement, their use also raises an increasing number of societal concerns, such as “Matthew effects”, “filter bubbles”, and the overall lack of transparency. We argue that focusing on transparency for content-providers is an under-explored avenue. As such, we designed a simulation framework called SIREN 1 (SImulating Recommender Effects in online News environments), that allows content providers to (i) select and parameterize different recommenders and (ii) analyze and visualize their effects with respect to two diversity metrics. Taking the U.S. news media as a case study, we present an analysis on the recommender effects with respect to long-tail novelty and unexpectedness using SIREN. Our analysis offers a number of interesting findings, such as the similar potential of certain algorithmically simple (item-based k-Nearest Neighbour) and sophisticated strategies (based on Bayesian Personalized Ranking) to increase diversity over time. Overall, we argue that simulating the effects of recommender systems can help content providers to make more informed decisions when choosing algorithmic recommenders, and as such can help mitigate the aforementioned societal concerns.
Original languageEnglish
Title of host publicationFAT* 2019
Subtitle of host publicationProceedings of the 2019 Conference on Fairness, Accountability, and Transparency
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery (ACM)
Pages150-159
Number of pages10
ISBN (Print)978-1-4503-6125-5
DOIs
Publication statusPublished - 2019
EventFAT* 2019: ACM Conference on Fairness, Accountability, and Transparency - Atlanta, United States
Duration: 29 Jan 201931 Jan 2019

Conference

ConferenceFAT* 2019
CountryUnited States
CityAtlanta
Period29/01/1931/01/19

ID: 47509396