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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.
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
I have a repository that could help you.
https://github.com/ArthurFortes/Datasets-for-Recommneder-Systems/
