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.gitattributes
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@@ -101,3 +101,5 @@ Time[[:space:]]Series/short_term_forecast/m4/Monthly-train.csv filter=lfs diff=l
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Time[[:space:]]Series/short_term_forecast/m4/Quarterly-train.csv filter=lfs diff=lfs merge=lfs -text
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Time[[:space:]]Series/short_term_forecast/m4/Yearly-train.csv filter=lfs diff=lfs merge=lfs -text
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Time[[:space:]]Series/short_term_forecast/m4/submission-Naive2.csv filter=lfs diff=lfs merge=lfs -text
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Time[[:space:]]Series/short_term_forecast/m4/Quarterly-train.csv filter=lfs diff=lfs merge=lfs -text
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Time[[:space:]]Series/short_term_forecast/m4/Yearly-train.csv filter=lfs diff=lfs merge=lfs -text
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Time[[:space:]]Series/short_term_forecast/m4/submission-Naive2.csv filter=lfs diff=lfs merge=lfs -text
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Recsys/RecBole/ml-1m/ml-1m/ratings.dat filter=lfs diff=lfs merge=lfs -text
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Recsys/RecBole/ml-1m/ml-1m.inter filter=lfs diff=lfs merge=lfs -text
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Recsys/RecBole/ml-1m/ml-1m.inter
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version https://git-lfs.github.com/spec/v1
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oid sha256:e943abb91013a54c385828fdf5ab4ce49e957ca3a772adb30cde2a7d5539b389
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Recsys/RecBole/ml-1m/ml-1m.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:a6898adb50b9ca05aa231689da44c217cb524e7ebd39d264c56e2832f2c54e20
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size 5917549
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Recsys/RecBole/ml-1m/ml-1m/README
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SUMMARY
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================================================================================
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These files contain 1,000,209 anonymous ratings of approximately 3,900 movies
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made by 6,040 MovieLens users who joined MovieLens in 2000.
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+
USAGE LICENSE
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================================================================================
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Neither the University of Minnesota nor any of the researchers
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involved can guarantee the correctness of the data, its suitability
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for any particular purpose, or the validity of results based on the
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use of the data set. The data set may be used for any research
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purposes under the following conditions:
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* The user may not state or imply any endorsement from the
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University of Minnesota or the GroupLens Research Group.
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* The user must acknowledge the use of the data set in
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publications resulting from the use of the data set
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(see below for citation information).
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* The user may not redistribute the data without separate
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permission.
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* The user may not use this information for any commercial or
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revenue-bearing purposes without first obtaining permission
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from a faculty member of the GroupLens Research Project at the
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University of Minnesota.
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If you have any further questions or comments, please contact GroupLens
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<grouplens-info@cs.umn.edu>.
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CITATION
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================================================================================
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To acknowledge use of the dataset in publications, please cite the following
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paper:
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F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History
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and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4,
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Article 19 (December 2015), 19 pages. DOI=http://dx.doi.org/10.1145/2827872
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ACKNOWLEDGEMENTS
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================================================================================
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Thanks to Shyong Lam and Jon Herlocker for cleaning up and generating the data
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set.
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FURTHER INFORMATION ABOUT THE GROUPLENS RESEARCH PROJECT
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================================================================================
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The GroupLens Research Project is a research group in the Department of
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Computer Science and Engineering at the University of Minnesota. Members of
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the GroupLens Research Project are involved in many research projects related
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to the fields of information filtering, collaborative filtering, and
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recommender systems. The project is lead by professors John Riedl and Joseph
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Konstan. The project began to explore automated collaborative filtering in
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1992, but is most well known for its world wide trial of an automated
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collaborative filtering system for Usenet news in 1996. Since then the project
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has expanded its scope to research overall information filtering solutions,
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integrating in content-based methods as well as improving current collaborative
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filtering technology.
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Further information on the GroupLens Research project, including research
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publications, can be found at the following web site:
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http://www.grouplens.org/
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GroupLens Research currently operates a movie recommender based on
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collaborative filtering:
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http://www.movielens.org/
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RATINGS FILE DESCRIPTION
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================================================================================
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All ratings are contained in the file "ratings.dat" and are in the
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following format:
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UserID::MovieID::Rating::Timestamp
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- UserIDs range between 1 and 6040
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- MovieIDs range between 1 and 3952
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- Ratings are made on a 5-star scale (whole-star ratings only)
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- Timestamp is represented in seconds since the epoch as returned by time(2)
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- Each user has at least 20 ratings
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USERS FILE DESCRIPTION
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================================================================================
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User information is in the file "users.dat" and is in the following
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format:
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UserID::Gender::Age::Occupation::Zip-code
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All demographic information is provided voluntarily by the users and is
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not checked for accuracy. Only users who have provided some demographic
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information are included in this data set.
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- Gender is denoted by a "M" for male and "F" for female
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- Age is chosen from the following ranges:
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* 1: "Under 18"
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* 18: "18-24"
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* 25: "25-34"
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* 35: "35-44"
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* 45: "45-49"
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* 50: "50-55"
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* 56: "56+"
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- Occupation is chosen from the following choices:
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* 0: "other" or not specified
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* 1: "academic/educator"
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* 2: "artist"
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* 3: "clerical/admin"
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* 4: "college/grad student"
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* 5: "customer service"
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* 6: "doctor/health care"
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* 7: "executive/managerial"
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* 8: "farmer"
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* 9: "homemaker"
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* 10: "K-12 student"
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* 11: "lawyer"
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* 12: "programmer"
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* 13: "retired"
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* 14: "sales/marketing"
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* 15: "scientist"
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* 16: "self-employed"
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* 17: "technician/engineer"
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* 18: "tradesman/craftsman"
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* 19: "unemployed"
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* 20: "writer"
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MOVIES FILE DESCRIPTION
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================================================================================
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Movie information is in the file "movies.dat" and is in the following
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format:
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MovieID::Title::Genres
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- Titles are identical to titles provided by the IMDB (including
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year of release)
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- Genres are pipe-separated and are selected from the following genres:
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* Action
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* Adventure
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* Animation
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* Children's
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* Comedy
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* Crime
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* Documentary
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* Drama
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* Fantasy
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* Film-Noir
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* Horror
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* Musical
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* Mystery
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* Romance
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* Sci-Fi
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* Thriller
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* War
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* Western
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- Some MovieIDs do not correspond to a movie due to accidental duplicate
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entries and/or test entries
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- Movies are mostly entered by hand, so errors and inconsistencies may exist
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Recsys/RecBole/ml-1m/ml-1m/movies.dat
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The diff for this file is too large to render.
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Recsys/RecBole/ml-1m/ml-1m/ratings.dat
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version https://git-lfs.github.com/spec/v1
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oid sha256:506d64ca44484487c11dc2d9a28de5c54948213e6b96285e298afe28d6ea4e0f
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size 24594131
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Recsys/RecBole/ml-1m/ml-1m/users.dat
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The diff for this file is too large to render.
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