research-article

SIREN: A Simulation Framework for Understanding the Effects of Recommender Systems in Online News Environments

Authors:

Dimitrios Bountouridis,

Jaron Harambam,

Mykola Makhortykh,

Mónica Marrero,

Claudia HauffAuthors Info & Claims

FAT* '19: Proceedings of the Conference on Fairness, Accountability, and Transparency

Pages 150 - 159

https://doi.org/10.1145/3287560.3287583

Published: 29 January 2019 Publication History

Abstract

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 SIREN1 (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.

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Index Terms

SIREN: A Simulation Framework for Understanding the Effects of Recommender Systems in Online News Environments

Recommendations

A framework for diversifying recommendation lists by user interest expansion

Recommender systems have been widely used to discover users' preferences and recommend interesting items to users during this age of information overload. Researchers in the field of recommender systems have realized that the quality of a top-N ...
Injecting Semantic Diversity in Top-N Recommender Systems Using Determinantal Point Processes and Curated Lists
UMAP '18: Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization

Top-N Recommender Systems usually suffer from intra-list diversity as they are tailored for relevance and predicted rating accuracy. This problem is magnified in the case of cold start setting - resulting in users being restricted to popular set of ...
A Clustering Approach for Personalizing Diversity in Collaborative Recommender Systems
UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

Much of the focus of recommender systems research has been on the accurate prediction of users' ratings for unseen items. Recent work has suggested that objectives such as diversity and novelty in recommendations are also important factors in the ...

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cover image ACM Conferences

FAT* '19: Proceedings of the Conference on Fairness, Accountability, and Transparency

January 2019

388 pages

ISBN:9781450361255

DOI:10.1145/3287560

Copyright © 2019 ACM.

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Published: 29 January 2019

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FAT* '19: Conference on Fairness, Accountability, and Transparency

January 29 - 31, 2019

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