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Deep learning techniques have become the method of choice for researchersworking on algorithmic aspects of recommender systems. With the stronglyincreased interest in machine learning in general, it has, as a result, becomedifficult 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 recentpublications point out problems in today's research practice in applied machinelearning, e.g., in terms of the reproducibility of the results or the choice ofthe baselines when proposing new models. In this work, we report the results ofa systematic analysis of algorithmic proposals for top-n recommendation tasks.Specifically, we considered 18 algorithms that were presented at top-levelresearch conferences in the last years. Only 7 of them could be reproduced withreasonable effort. For these methods, it however turned out that 6 of them canoften be outperformed with comparably simple heuristic methods, e.g., based onnearest-neighbor or graph-based techniques. The remaining one clearlyoutperformed the baselines but did not consistently outperform a well-tunednon-neural linear ranking method. Overall, our work sheds light on a number ofpotential problems in today's machine learning scholarship and calls forimproved scientific practices in this area. Source code of our experiments andfull results are available at:https://github.com/MaurizioFD/RecSys2019_DeepLearning_Evaluation.

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