A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research

Understand

The design of algorithms that generate personalized ranked item lists is a central topic of research in the field of recommender systems.

• In the past few years, in particular, approaches based on deep learning (neural) techniques have become dominant in the literature.
• For all of them, substantial progress over the state-of-the-art is claimed.
• However, indications exist of certain problems in today's research practice, e.g., with respect to the choice and optimization of the baselines used for comparison, raising questions about the published claims.