Optimizing the Selection of Recommendation Carousels with Quantum Computing

It has been long known that quantum computing has the potential to revolutionize the way we find solutions of problems that are difficult to solve on classical computers. It was only recently that small but functional quantum computers have become available on the cloud, allowing to test their potential. In this paper we propose to leverage their capabilities to address an important task for recommender systems providers, the optimal selection of recommendation carousels. In many video-on-demand and music streaming services the user is provided with a homepage containing several recommendation lists, i.e., carousels, each built with a certain criteria (e.g., artist, mood, Action movies etc.). Choosing which set of carousels to display is a difficult problem because it needs to account for how the different recommendation lists interact, e.g., avoiding duplicate recommendations, and how they help the user explore the catalogue. We focus in particular on the adiabatic computing paradigm and use the D-Wave quantum annealer, which is able to solve NP-hard optimization problems, can be programmed by classical operations research tools and is freely available on the cloud. We propose a formulation of the carousel selection problem for black box recommenders, that can be solved effectively on a quantum annealer and has the advantage of being simple. We discuss its effectiveness, limitations and possible future directions of development.