A/B testing is a powerful tool because of its design simplicity and ease of setup.

An Zhang and Leheng Sheng contribute equally to this work. Zhibo Cai is the corresponding author.

While other areas of machine learning have seen more and more automation, designing a high-performing recommender system still requires a high level of human effort. Furthermore, recent work has shown that modern recommender system algorithms do not always improve over well-tuned baselines. A natural follow-up question is, "how do we choose the right algorithm for anew dataset and performance metric?" In this work, we start by giving the first large-scale study ofrecommender system approaches bycomparing 24algorithms and100 sets of hyperparameters across 85 datasets and 315 metrics. We find that the best algorithms and hyperparameters are highly dependent on the dataset and performance metric.

Despite their empirical success, there is alack oftheoretical explorations such asgeneralization properties.

Rotten Tomatoes denies claims the “Melania" film's 99% audience score was inflated, as the film shows an over 93-point gap between audience and critics.