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MovieLens is a recommender system and virtual community website that recommends movies for its users to watch, based on their film preferences using collaborative filtering. GroupLens Research, a research lab in the Department of Computer Science and Engineering at the University of Minnesota, created MovieLens in 1997[1] to gather research data on personalized recommendations.[2]


MovieLens was not the first recommender system created by GroupLen. In May 1996, GroupLens formed a commercial venture called Net Perceptions, which had clients including E! Online and E! Online used Net Perceptions' services to create the recommendation system for,[2] while used the company's technology to form its early recommendation engine for consumer purchases.[3]

When the EachMovie[4] site closed in 1997, the researchers behind it released the anonymous rating data they had collected, for other researchers to use. The GroupLens Research team, led by Brent Dahlen and Jon Herlocker, used this data set to jumpstart a new movie recommendation site called MovieLens. Since then, MovieLens has been a very visible research platform, including a detailed discussion in a New Yorker article by Malcolm Gladwell,[5] and a report in a full episode of ABC Nightline.[6]


MovieLens bases its recommendations on input provided by users of the website, such as movie ratings.[1] The uses a variety of recommendation algorithms, including collaborative filtering algorithms such as item-item,[7] user-user, and regularized SVD.[8] In addition, to address the cold-start problem for new users, MovieLens uses preference elicitation methods.[9] The system asks new users to rate how much they enjoy watching various groups of movies (for example, movies with dark humor, or romantic movies). These group preferences allow the system to make initial recommendations before the user has rated many movies on the website.

For each user, MovieLens predicts how the user will rate any given movie on the website.[10] Based on these predicted ratings, the system recommends movies the user is likely to rate highly. The website suggests that users rate as many films that they have seen as possible so that the recommendations given will be more accurate, since the system would have a better sample of the user's film tastes.[2]

In addition to movie recommendations, MovieLens also provides information on individual films, such as lists of actors and directors. Users may also submit and rate tags (a form of metadata, such as "based on a book", "too long", or "campy"), which may be used to increase the film recommendations system's accuracy.[2]


By September 1997, the website had over 50,000 users.[2] When the Akron Beacon Journal's Paula Schleis tried out the website, she was surprised at how accurate the website was in terms of recommending new films for her to watch based on her film tastes.[10]

Outside of the realm of movie recommendations, data from MovieLens has been used by Solution by Simulation to make Oscar predictions.[11]


  1. ^ a b Schofield, Jack (2003-05-22). "Land of Gnod". The Guardian. London. 
  2. ^ a b c d e Ojeda-Zapata, Julio (1997-09-15). "New Site Personalizes Movie Reviews". St. Paul Pioneer Press. p. 3E. 
  3. ^ Booth, Michael (2005-01-30). "How do computers know so much about us?". The Denver Post. p. F01. 
  4. ^ Lim, Myungeun; Kim, Juntae (2001). "An Adaptive Recommendation System with a Coordinator Agent". Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development. Asia-Pacific Conference on Web Intelligence. Lecture Notes in Computer Science. Springer Berlin/Heidelberg. pp. 438–442. doi:10.1007/3-540-45490-X_56. ISBN 978-3-540-42730-8. Retrieved 2009-12-30. 
  5. ^ Gladwell, Malcolm (October 4, 1999). "Annals of Marketing: The Science of the Sleeper: How the Information Age Could Blow Away the Blockbuster". New Yorker. 75 (29): 48–55. Retrieved 2009-12-29. 
  6. ^ Krulwich, Robert (December 10, 1999). "ABC Nightline: Soulmate". ABC. 
  7. ^ Sarwar, Badrul, et al. "Item-based collaborative filtering recommendation algorithms." Proceedings of the 10th international conference on World Wide Web. ACM, 2001.
  8. ^ Ekstrand, Michael D. Towards Recommender Engineering Tools and Experiments for Identifying Recommender Differences. Diss. UNIVERSITY OF MINNESOTA, 2014.
  9. ^ Chang, Shuo, F. Maxwell Harper, and Loren Terveen. "Using Groups of Items to Bootstrap New Users in Recommender Systems." Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing. ACM, 2015.
  10. ^ a b Schleis, Paula (2000-11-13). "Site Lets Everybody be a Critic". Akron Beacon Journal. p. D2. 
  11. ^ Hickey, Walt. "Do Your Oscar Predictions Stack Up? Here's What The Data Says." FiveThirtyEight. N.p., 18 Feb. 2016. Web. 08 Mar. 2016. <>

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