Search for: All records

Abstract Personalized news experiences powered by recommender systems permeate our lives and have the potential to influence not only our opinions, but also our decisions. At the same time, the content and viewpoints contained within news recommendations are driven by multiple factors, including both personalization and editorial selection. Explanations could help users gain a better understanding of the factors contributing to the news items selected for them to read. Indeed, recent works show that explanations are essential for users of news recommenders to understand their consumption preferences and set intentions in line with their goals, such as goals for knowledge development and increased diversity of content or viewpoints. We give examples of such works on explanation and interactive interface interventions which have been effective in influencing readers' consumption intentions and behaviors in news recommendations. However, the state‐of‐the‐art in news recommender systems currently fall short in terms of evaluating such interventions in live systems, limiting our ability to measure their true impact on user behavior and opinions. To help understand the true benefit of these interfaces, we therefore call for improving the realism of studies for news. 

This tutorial provides practical training in designing and conduct- ing online user experiments with recommender systems, and in statistically analyzing the results of such experiments. It covers the development of a research question and hypotheses, the selection of study participants, the manipulation of system aspects and mea- surement of behaviors, perceptions and user experiences, and the evaluation of subjective measurement scales and study hypotheses. Interested parties can find the slides, example datset, and other resources at https://www.usabart.nl/QRMS/.