This dataset (ml-20m) describes 5-star rating and free-text tagging activity from MovieLens, a movie recommendation service. It contains 20000263 ratings and 465564 tag applications across 27278 movies. These data were created by 138493 users between January 09, 1995 and March 31, 2015. This dataset was generated on March 31, 2015.
Users were selected at random for inclusion. All selected users had rated at least 20 movies. No demographic information is included. Each user is represented by an id, and no other information is provided.
The data are contained in four files, links.csv, movies.csv, ratings.csv and tags.csv. More details about the contents and use of all these files follows.
This and other GroupLens data sets are publicly available for download at http://grouplens.org/datasets/.
Neither the University of Minnesota nor any of the researchers involved can guarantee the correctness of the data, its suitability for any particular purpose, or the validity of results based on the use of the data set. The data set may be used for any research purposes under the following conditions:
In no event shall the University of Minnesota, its affiliates or employees be liable to you for any damages arising out of the use or inability to use these programs (including but not limited to loss of data or data being rendered inaccurate).
If you have any further questions or comments, please email grouplens-info@cs.umn.edu
To acknowledge use of the dataset in publications, please cite the following paper:
F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4, Article 19 (December 2015), 19 pages. DOI=http://dx.doi.org/10.1145/2827872
GroupLens is a research group in the Department of Computer Science and Engineering at the University of Minnesota. Since its inception in 1992, GroupLens's research projects have explored a variety of fields including:
GroupLens Research operates a movie recommender based on collaborative filtering, MovieLens, which is the source of these data. We encourage you to visit http://movielens.org to try it out! If you have exciting ideas for experimental work to conduct on MovieLens, send us an email at grouplens-info@cs.umn.edu - we are always interested in working with external collaborators.
We encourage you to verify that the dataset you have on your computer is identical to the ones hosted at grouplens.org. This is an important step if you downloaded the dataset from a location other than grouplens.org, or if you wish to publish research results based on analysis of the MovieLens dataset.
We provide a MD5 checksum with the same name as the downloadable .zip file, but with a .md5 file extension. To verify the dataset:
# on linux
md5sum ml-20m.zip; cat ml-20m.zip.md5
# on OSX
md5 ml-20m.zip; cat ml-20m.zip.md5
# windows users can download a tool from Microsoft (or elsewhere) that verifies MD5 checksums
Check that the two lines of output contain the same hash value.
The dataset files are written as comma-separated values files with a single header row. Columns that contain commas (,) are escaped using double-quotes ("). These files are encoded as UTF-8. If accented characters in movie titles or tag values (e.g. Misérables, Les (1995)) display incorrectly, make sure that any program reading the data, such as a text editor, terminal, or script, is configured for UTF-8.
MovieLens users were selected at random for inclusion. Their ids have been anonymized. User ids are consistent between ratings.csv and tags.csv (i.e., the same id refers to the same user across the two files).
Only movies with at least one rating or tag are included in the dataset. These movie ids are consistent with those used on the MovieLens web site (e.g., id 1 corresponds to the URL https://movielens.org/movies/1). Movie ids are consistent between ratings.csv, tags.csv, movies.csv, and links.csv (i.e., the same id refers to the same movie across these four data files).
All ratings are contained in the file ratings.csv. Each line of this file after the header row represents one rating of one movie by one user, and has the following format:
userId,movieId,rating,timestamp
The lines within this file are ordered first by userId, then, within user, by movieId.
Ratings are made on a 5-star scale, with half-star increments (0.5 stars - 5.0 stars).
Timestamps represent seconds since midnight Coordinated Universal Time (UTC) of January 1, 1970.
All tags are contained in the file tags.csv. Each line of this file after the header row represents one tag applied to one movie by one user, and has the following format:
userId,movieId,tag,timestamp
The lines within this file are ordered first by userId, then, within user, by movieId.
Tags are user-generated metadata about movies. Each tag is typically a single word or short phrase. The meaning, value, and purpose of a particular tag is determined by each user.
Timestamps represent seconds since midnight Coordinated Universal Time (UTC) of January 1, 1970.
Movie information is contained in the file movies.csv. Each line of this file after the header row represents one movie, and has the following format:
movieId,title,genres
Movie titles are entered manually or imported from https://www.themoviedb.org/, and include the year of release in parentheses. Errors and inconsistencies may exist in these titles.
Genres are a pipe-separated list, and are selected from the following:
Identifiers that can be used to link to other sources of movie data are contained in the file links.csv. Each line of this file after the header row represents one movie, and has the following format:
movieId,imdbId,tmdbId
movieId is an identifier for movies used by https://movielens.org. E.g., the movie Toy Story has the link https://movielens.org/movies/1.
imdbId is an identifier for movies used by http://www.imdb.com. E.g., the movie Toy Story has the link http://www.imdb.com/title/tt0114709/.
tmdbId is an identifier for movies used by https://www.themoviedb.org. E.g., the movie Toy Story has the link https://www.themoviedb.org/movie/862.
Use of the resources listed above is subject to the terms of each provider.
Prior versions of the MovieLens dataset included either pre-computed cross-folds or scripts to perform this computation. We no longer bundle either of these features with the dataset, since most modern toolkits provide this as a built-in feature. If you wish to learn about standard approaches to cross-fold computation in the context of recommender systems evaluation, see LensKit for tools, documentation, and open-source code examples.