Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches

Understand

Deep learning techniques have become the method of choice for researchers working on algorithmic aspects of recommender systems.

• With the strongly increased interest in machine learning in general, it has, as a result, become difficult to keep track of what represents the state-of-the-art at the moment, e.g., for top-n recommendation tasks.
• At the same time, several recent publications point out problems in today's research practice in applied machine learning, e.g., in terms of the reproducibility of the results or the choice of the baselines when proposing new models.
• In this work, we report the results of a systematic analysis of algorithmic proposals for top-n recommendation tasks.