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
Recommender systems offer great opportunity not only for users to discover new content, but also for the providers of that content to find new audience, followers, and fans. Users often come to a recommender system with certain expectations about what it will recommend to them, and a recommender system that is optimized for creating opportunities for content creators may provide recommendations that are very different from what a user is expecting. We hypothesize that some users’ expectations have a much wider range of acceptability than others, and users with more ”receptivity” to subversion of their expectations are likely to accept such divergence in the recommended content. Understanding users’ responses to such recommendations is vital to platforms that need to serve multiple stakeholders. In this work we investigate logged behavioral responses of users of an audio streaming platform to recommendations that deviate from their expectation, or “divergent” recommendations. We present three classes of listener response to divergent recommendations that can be identified in interaction logs with the aim of predicting which users can be targeted for future divergent recommendations. We derive a number of user characteristics based on user’s music consumption which we think are predictive of user’s receptivity, train models to predict receptivity of these users, and run a live A/B test to validate our approach by correlating with engagement.
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