TY - GEN
T1 - Recommenders with a Mission
T2 - 6th ACM SIGIR Conference on Human Information Interaction and Retrieval, CHIIR 2021
AU - Vrijenhoek, Sanne
AU - Kaya, Mesut
AU - Metoui, Nadia
AU - Möller, Judith
AU - Odijk, Daan
AU - Helberger, Natali
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - diversity
KW - news recommender systems
KW - normative framework
UR - http://www.scopus.com/inward/record.url?scp=85102737135&partnerID=8YFLogxK
U2 - 10.1145/3406522.3446019
DO - 10.1145/3406522.3446019
M3 - Conference contribution
AN - SCOPUS:85102737135
T3 - CHIIR 2021 - Proceedings of the 2021 Conference on Human Information Interaction and Retrieval
SP - 173
EP - 183
BT - CHIIR 2021 - Proceedings of the 2021 Conference on Human Information Interaction and Retrieval
PB - ACM
Y2 - 14 March 2021 through 19 March 2021
ER -