Unbiased Identification of Broadly Appealing Content Using a Pure Exploration Infinitely-Armed Bandit Strategy
Maryam Aziz, Jesse Anderton, Kevin Jamieson, Alice Wang, Hugues Bouchard, Javed Aslam
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.
Maryam Aziz, Jesse Anderton, Kevin Jamieson, Alice Wang, Hugues Bouchard, Javed Aslam
Dmitrii Moor, Yi Yuan, Rishabh Mehrotra, Zhenwen Dai, Mounia Lalmas
Enrico Palumbo, Andreas Damianou, Alice Wang, Alva Liu, Ghazal Fazelnia, Francesco Fabbri, Rui Ferreira, Fabrizio Silvestri, Hugues Bouchard, Claudia Hauff, Mounia Lalmas, Ben Carterette, Praveen Chandar, David Nyhan