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. It contains about 11 million ratings for about 8500 movies. 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 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. It contains about 11 million ratings for about 8500 movies. 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 collaborative study between Carnegie Mellon University, University of Michigan, University of Minnesota, and University of Pittsburgh, 'Using Social Psychology to Motivate Contributions to Online Communities'. During Spring in 2015, a search for 'movielens' produced 2,750 results in Google Books and 7,580 in Google Scholar. 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. For each user, MovieLens predicts how the user will rate any given movie on the website. Based on these predicted ratings, the system recommends movies that the user is likely to rate highly. The website suggests that users rate as many fully watched films as possible, so that the recommendations given will be more accurate, since the system would then have a better sample of the user's film tastes. However, MovieLens' rating incentive approach is not always particularly effective, as researchers found more than 20% of the movies listed in the system have so few ratings that the recommender algorithms cannot make accurate predictions about whether subscribers will like them or not. The recommendations on movies cannot contain any marketing values that can tackle the large number of movie ratings as a 'seed dataset'. In addition to movie recommendations, MovieLens also provides information on individual films, such as the list of actors and directors of each film. 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. The ratings in MovieLens could happen any time, in fact, it could happen years later after watching a movie. Users would often enter numerous ratings at once hoping that they would get more personalized recommendations or just for satisfaction. By September 1997, the website had reached over 50,000 users. 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.