Challenges in Translating Research to Practice for Evaluating Fairness & Bias in Recommendation Systems
Lex Beattie, Dan Taber, Henriette Cramer
Podcasts are a popular medium for rapid dissemination of information, entertainment, and casual conversations. Content aggregators are taking an increased interest in recommending podcasts to listeners to help them build larger audiences. With many podcasts released every day, many podcasts that would be of interest to listeners remain underserved by these recommendation systems. In this paper, we study variables related to podcast appeal to listeners selected at random in a large online study, in a production setting, involving more than five million recommendations. We present the results of two observational studies, which suggests that underserved podcast have the potential to grow their audiences. To mitigate the rich-get-richer effect, we propose leveraging semantic information, via means of knowledge graphs, to recommend underserved podcasts to listeners. Finally, we conduct empirical experiments that show our method is effective at recommending underserved podcasts, in comparison to baseline methods that rely on listening behavior.
Lex Beattie, Dan Taber, Henriette Cramer
Maryam Aziz, Jesse Anderton, Kevin Jamieson, Alice Wang, Hugues Bouchard, Javed Aslam
M Iftekhar Tanveer, Diego Casabuena, Jussi Karlgren, Rosie Jones