Paper Info
Title
SIREN - A Simulation Framework for Understanding the Effects of Recommender Systems in Online News Environments.
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 SI REN1 (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.
Year
DOI
Venue
2019
10.1145/3287560.3287583
FAT*'19: PROCEEDINGS OF THE 2019 CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY
Keywords
DocType
Citations 
recommender systems,diversity,news media,simulation
Conference
2
PageRank 
References 
Authors
0.38
0
6
Name
Order
Citations
PageRank
Dimitrios Bountouridis140.74
Jaron Harambam251.43
Mykola Makhortykh352.45
Mónica Marrero420.38
Nava Tintarev542842.25
Claudia Hauff679065.52