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I'd like to test a new algorithm for collaborative filtering. A typical use case is to recommend movies based on the preferences of users similar to the specific user.

What are some common benchmark datasets that researchers often use to test their algorithms? I know that within Computer Vision people often use MNIST or CIFAR, but I haven't found similar datasets for collaborative filtering.

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    $\begingroup$ Did you take a look at Netflix prize dataset? Yes, the competition is long over and it has been pulled from the official website due to some privacy reasons. You can still try to find it in other locations. $\endgroup$ – Vladislavs Dovgalecs Mar 23 '16 at 15:49
  • $\begingroup$ Kaggle.com has a bunch. Just search for 'recommendation in:dataset' or 'recommendation in:competition'. $\endgroup$ – ran8 Aug 4 '17 at 13:05
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The obvious answer would be the Netflix prize dataset, there is a lot of research into it and most CF algorithms have known scores in it.

There are other available datasets that are usually used as benchmarks:

  • Movie lens Dataset: a 20 million ratings dataset used for benchmarking CF algorithms;

  • Jester Dataset: a joke recommendation dataset with more than 6 million ratings;

  • You can find many more datasets in this link

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I have a repository that could help you.

https://github.com/ArthurFortes/Datasets-for-Recommneder-Systems/

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    $\begingroup$ Please do not post link-only answers, answers should be self-contained. I recommend editing your answer to add at least a few of the information the link provides, and then provide the link for further exploration. $\endgroup$ – Mephy Nov 3 '17 at 18:29

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