@inproceedings{a1949ffc40524e9cbbae4436b88313ae,
title = "Recommenders with a Mission: Assessing Diversity in News Recommendations",
abstract = "News recommenders help users to find relevant online content and have the potential to fulfilla crucial role in a democratic society, directing the scarce attention of citizens towards the information that is most important to them. Simultaneously, recent concerns about so-called filter bubbles, misinformation and selective exposure are symptomatic of the disruptive potential of these digital news recommenders. Recommender systems can make or break filter bubbles, and as such can be instrumental in creating either a more closed or a more open internet. Current approaches to evaluating recommender systems are often focused on measuring an increase in user clicks and short-term engagement, rather than measuring the user's longer term interest in diverse and important information. This paper aims to bridge the gap between normative notions of diversity, rooted in democratic theory, and quantitative metrics necessary for evaluating the recommender system. We propose a set ofmetrics grounded in social science interpretations of diversity and suggest ways for practical implementations.",
keywords = "diversity, news recommender systems, normative framework",
author = "Sanne Vrijenhoek and Mesut Kaya and Nadia Metoui and Judith M{\"o}ller and Daan Odijk and Natali Helberger",
year = "2021",
doi = "10.1145/3406522.3446019",
language = "English",
series = "CHIIR 2021 - Proceedings of the 2021 Conference on Human Information Interaction and Retrieval",
publisher = "Association for Computing Machinery (ACM)",
pages = "173--183",
booktitle = "CHIIR 2021 - Proceedings of the 2021 Conference on Human Information Interaction and Retrieval",
address = "United States",
note = "6th ACM SIGIR Conference on Human Information Interaction and Retrieval, CHIIR 2021 ; Conference date: 14-03-2021 Through 19-03-2021",
}