  Numerical evaluations with comparisons to baselines play a central role when
judging research in recommender systems. In this paper, we show that running
baselines properly is difficult. We demonstrate this issue on two extensively
studied datasets. First, we show that results for baselines that have been used
in numerous publications over the past five years for the Movielens 10M
benchmark are suboptimal. With a careful setup of a vanilla matrix
factorization baseline, we are not only able to improve upon the reported
results for this baseline but even outperform the reported results of any newly
proposed method. Secondly, we recap the tremendous effort that was required by
the community to obtain high quality results for simple methods on the Netflix
Prize. Our results indicate that empirical findings in research papers are
questionable unless they were obtained on standardized benchmarks where
baselines have been tuned extensively by the research community.
