The Quest for Recommendations with Online Success

Olivier Jeunen

Abstract. Recommender Systems research often focuses on static datasets, with static models and static evaluation scenarios. Practical applications, however, often dictate highly dynamic environments for the recommender to perform in. In this talk, I will present some recent advances that aim to bridge this gap. The first part of this talk will focus on online learning, and how existing state-of-the-art static approaches to recommendation can be adapted to thrive in online settings. Then, I will question some of the core assumptions of the "next-item prediction" paradigm that the field has focused on, and motivate the "bandit learning" paradigm as an alternative that allows us to keep a clear focus on online objectives. In order to achieve true online success, we additionally need to consider the impact of our recommendations and biases that arise from feedback loops.

Olivier Jeunen recently received his PhD from the University of Antwerp, Belgium. His research interests revolve around machine learning and its applications in information retrieval and recommendation: tackling real-world problems from sound theoretical foundations. His recent work focuses on bandit learning for recommendation use-cases, covering aspects from scalability and evaluation to core algorithms. Pursuing this synthesis of theory and application throughout his studies, he has collaborated with the Criteo AI Lab, Facebook, and Spotify Research.