MovieLens Benchmark Task

A MovieLens Benchmark Task is a benchmark item recommendations dataset from MovieLens website.

• Context:
  • It can (typically) have Movie Records, such as
1|Toy Story (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Toy%20Story%20(1995) |0|0|0|1|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0
2|GoldenEye (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?GoldenEye%20(1995) |0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
3|Four Rooms (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Four%20Rooms%20(1995) |0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
...
  • It can be based on 1 to 5 Movie Ratings.
  • It can associated to a MovieLens Data Usage License [1].
• Example(s):
  • MovieLens 100K [2], which contains 943 users and 1,682 items, which has 100,000 ratings with a density of 6.3%.
  • MovieLens 1M [3], which contains 6,040 users and 3,952 items, which has 1,000,209 ratings with a density of 3.8%.
  • MovieLens 10M.
  • MovieLens 20M [4], which contains 20,000,263 ratings and 465,564 tag applications across 27,278 movies. These data were created by 138,493 users between 1995-01-09 and 2015-03-31. This dataset was generated on October 17, 2016.
• Counter-Example(s):
  • Netflix Prize Dataset.
• See: Data-Driven Item Recommendations.

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References

2017

• http://grouplens.org/datasets/movielens/
  • QUOTE: GroupLens Research has collected and made available rating data sets from the MovieLens web site (http://movielens.org). The data sets were collected over various periods of time, depending on the size of the set. Before using these data sets, please review their README files for the usage licenses and other details.

2016

• (Harper & Konstan, 2016) ⇒ F. Maxwell Harper, and Joseph A . Konstan. (2016). “The Movielens Datasets: History and Context.” Acm transactions on interactive intelligent systems (tiis) 5, no. 4
  • ABSTRACT: The MovieLens datasets are widely used in education, research, and industry. They are downloaded hundreds of thousands of times each year, reflecting their use in popular press programming books, traditional and online courses, and software. These datasets are a product of member activity in the MovieLens movie recommendation system, an active research platform that has hosted many experiments since its launch in 1997. This article documents the history of MovieLens and the MovieLens datasets. We include a discussion of lessons learned from running a long-standing, live research platform from the perspective of a research organization. We document best practices and limitations of using the MovieLens datasets in new research.

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