Showing posts with label Recommendation and ranking systems. Show all posts
Showing posts with label Recommendation and ranking systems. Show all posts

Friday, 17 April 2020

Recommendation and ranking systems

Recommendation and ranking systems
  • Movielens: Movie ratings dataset from the Movielens website, in various sizes ranging from demo to mid-size.
  • Million Song Dataset: Large, metadata-rich, open source dataset on Kaggle that can be good for people experimenting with hybrid recommendation systems.
  • Last.fm: Music recommendation dataset with access to underlying social network and other metadata that can be useful for hybrid systems.
  • Book-Crossing dataset:: From the Book-Crossing community. Contains 278,858 users providing 1,149,780 ratings about 271,379 books.
  • Jester: 4.1 million continuous ratings (-10.00 to +10.00) of 100 jokes from 73,421 users.
  • Netflix Prize:: Netflix released an anonymized version of their movie rating dataset; it consists of 100 million ratings, done by 480,000 users who have rated between 1 and all of the 17,770 movies. First major Kaggle style data challenge. Only available unofficially, as privacy issues arose.

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