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MovieLens is a web-based
recommender system A recommender system (RecSys), or a recommendation system (sometimes replacing ''system'' with terms such as ''platform'', ''engine'', or ''algorithm'') and sometimes only called "the algorithm" or "algorithm", is a subclass of information fi ...
and
virtual community A virtual community is a social network of individuals who connect through specific social media, potentially crossing geographical and political boundaries in order to pursue mutual interests or goals. Some of the most pervasive virtual commu ...
that recommends movies for its users to watch, based on their film preferences using
collaborative filtering Collaborative filtering (CF) is, besides content-based filtering, one of two major techniques used by recommender systems.Francesco Ricci and Lior Rokach and Bracha ShapiraIntroduction to Recommender Systems Handbook, Recommender Systems Handbo ...
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 GroupLens Research is a human–computer interaction research lab in the Department of Computer Science and Engineering (University of Minnesota), Department of Computer Science and Engineering at the University of Minnesota, University of Min ...
, a research lab in the Department of Computer Science and Engineering at the
University of Minnesota The University of Minnesota Twin Cities (historically known as University of Minnesota) is a public university, public Land-grant university, land-grant research university in the Minneapolis–Saint Paul, Twin Cities of Minneapolis and Saint ...
, in order to gather research data on personalized recommendations.


History

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 E! Entertainment Television is an American basic cable television network. It is owned by the NBCUniversal Media Group division of Comcast's NBCUniversal. The channel focuses primarily on pop culture, celebrity based reality shows and movies. ...
and
Amazon.com Amazon.com, Inc., doing business as Amazon, is an American multinational technology company engaged in e-commerce, cloud computing, online advertising, digital streaming, and artificial intelligence. Founded in 1994 by Jeff Bezos in Bellevu ...
. 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 A data set (or dataset) is a collection of data. In the case of tabular data, a data set corresponds to one or more table (database), database tables, where every column (database), column of a table represents a particular Variable (computer sci ...
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 Malcolm Timothy Gladwell (born 3 September 1963) is a Canadian journalist, author, and public speaker. He has been a staff writer for ''The New Yorker'' since 1996. He has published eight books. He is also the host of the podcast ''Revisionist ...
, 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.


Recommendations

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 Collaborative filtering (CF) is, besides content-based filtering, one of two major techniques used by recommender systems.Francesco Ricci and Lior Rokach and Bracha ShapiraIntroduction to Recommender Systems Handbook, Recommender Systems Handbo ...
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 Preference elicitation refers to the problem of developing a decision support system capable of generating recommendations to a user, thus assisting in decision making. It is important for such a system to model user's preferences accurately, fin ...
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 Metadata (or metainformation) is "data that provides information about other data", but not the content of the data itself, such as the text of a message or the image itself. There are many distinct types of metadata, including: * Descriptive ...
, such as "based on a book", "too long", or "campy"), which may be used to increase the film recommendations system's accuracy.


Reception

By September 1997, the website had reached over 50,000 users. When the ''
Akron Beacon Journal The ''Akron Beacon Journal'' is a morning newspaper in Akron, Ohio, United States. Owned by Gannett, it is the sole daily newspaper in Akron and is distributed throughout Northeast Ohio. The paper's coverage focuses on local news. The Beacon Jo ...
''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. Outside of the realm of movie recommendations, data from MovieLens has been used by Solution by Simulation to make Oscar predictions. Hickey, Walt. "Do Your Oscar Predictions Stack Up? Here's What The Data Says." FiveThirtyEight. N.p., 18 Feb. 2016. Web. 08 Mar. 2016.


Research

In 2004, a collaborative study with researchers from
Carnegie Mellon University Carnegie Mellon University (CMU) is a private research university in Pittsburgh, Pennsylvania, United States. The institution was established in 1900 by Andrew Carnegie as the Carnegie Technical Schools. In 1912, it became the Carnegie Institu ...
,
University of Michigan The University of Michigan (U-M, U of M, or Michigan) is a public university, public research university in Ann Arbor, Michigan, United States. Founded in 1817, it is the oldest institution of higher education in the state. The University of Mi ...
,
University of Minnesota The University of Minnesota Twin Cities (historically known as University of Minnesota) is a public university, public Land-grant university, land-grant research university in the Minneapolis–Saint Paul, Twin Cities of Minneapolis and Saint ...
and
University of Pittsburgh The University of Pittsburgh (Pitt) is a Commonwealth System of Higher Education, state-related research university in Pittsburgh, Pennsylvania, United States. The university is composed of seventeen undergraduate and graduate schools and colle ...
designed and tested incentives derived from the social psychology principles of
social loafing In social psychology, social loafing is the phenomenon of a person exerting less effort to achieve a goal when they work in a group than when working alone. It is seen as one of the main reasons groups are sometimes less productive than the combin ...
and
goal-setting Goal setting involves the development of an action plan designed in order to motivate and guide a person or group toward a goal. Goals are more deliberate than desires and momentary intentions. Therefore, setting goals means that a person has com ...
on MovieLens users. The researchers saw that under-contribution seemed to be a problem for the community and set up a study to discern the most effective way to motivate users to rate and review more films. The study executed two field experiments; one involved email messages that reminded users of the uniqueness of their contributions and the benefits that follow from them, and the other gave users a range of individual or group goals for contribution. The first experiment, based on the analysis of the MovieLens community’s cumulative response, found that users were more likely to contribute to the community when they were reminded of their uniqueness, leading them to think that their contributions are not duplicates of what other users are able to provide. Contrary to the researchers’ hypothesis, they also found that users were less likely to contribute when it was made salient to them the benefit they receive from rating or the benefit others receive when they rate. Lastly, they found no support for the relationship between uniqueness and benefit. The second experiment found that users were also more likely to contribute when they were given specific and challenging goals and were led to believe that their contributions were needed in order to accomplish the group’s goal. The study found that in this particular context, giving users group-level goals actually increased contributions compared to individual goals, where the researchers predicted that the reverse would be true due to the effects of social loafing. The relationship between goal difficulty and user contributions in both the group and individual cases gave weak evidence that beyond a certain difficulty threshold, performance drops, instead of plateaus as previously hypothesized in Locke and Latham’s goal-setting theory.


Datasets

GroupLens Research GroupLens Research is a human–computer interaction research lab in the Department of Computer Science and Engineering (University of Minnesota), Department of Computer Science and Engineering at the University of Minnesota, University of Min ...
, a human-computer interaction research lab at the
University of Minnesota The University of Minnesota Twin Cities (historically known as University of Minnesota) is a public university, public Land-grant university, land-grant research university in the Minneapolis–Saint Paul, Twin Cities of Minneapolis and Saint ...
, provides the rating data sets collected from MovieLens website for research use. The full data set contains 26,000,000 ratings and 750,000 tag applications applied to 45,000 movies by 270,000 users. It also includes tag genome data with 12 million relevance scores across 1,100 tags (Last updated 8/2017). There are a number of smaller datasets available with sizes of around 100,000 ratings, 1 million, 10 millions and 20 millions. There are many types of research conducted based on the MovieLens data sets. Liu et al. used MovieLens data sets to test the efficiency of an improved random walk algorithm by depressing the influence of large-degree objects. GroupLens has terms of use for the dataset, an
it accepts requests via the internet


References


External links

* {{official website Online film databases Recommender systems American film review websites Internet properties established in 1997 University of Minnesota software