MovieLens

MovieLens is a web-based recommender system and virtual community that recommends movies for its users to watch, based on their film preferences using collaborative filtering of members' movie ratings and movie reviews. MovieLens was created in 1997 by GroupLens Research, a research lab in the Department of Computer Science and Engineering at the University of Minnesota, in order to gather research data on personalized recommendations.

MovieLens was not the first recommender system created by GroupLens. In May 1996, GroupLens formed a commercial venture called Net Perceptions, which served clients that included E! Online and Amazon.com. E! Online used Net Perceptions' services to create the recommendation system for Moviefinder.com, while Amazon.com used the company's technology to form its early recommendation engine for consumer purchases.

When another movie recommendation site, eachmovie.org, closed in 1997, the researchers who built it publicly 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, which they chose to call MovieLens. Since its inception, MovieLens has become a very visible research platform: its data findings have been featured in a detailed discussion in a New Yorker article by Malcolm Gladwell, as well as a report in a full episode of ABC Nightline. Additionally, MovieLens data has been critical for several research studies, including a Carnegie Mellon University study, "Using Social Psychology to Motivate Contributions to Online Communities".

MovieLens bases its recommendations on input provided by users of the website, such as movie ratings. The site uses a variety of recommendation algorithms, including collaborative filtering algorithms such as item-item, user-user, and regularized SVD. In addition, to address the cold-start problem for new users, MovieLens uses preference elicitation methods. The system asks new users to rate how much they enjoy watching various groups of movies (for example, movies with dark humor, versus romantic comedies). The preferences recorded by this survey allow the system to make initial recommendations, even before the user has rated a large number of movies on the website.

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