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It contains about 11 million ratings for about 8500 movies. [1] MovieLens was created in 1997 by GroupLens Research, a research lab in the Department of Computer Science and Engineering at the University of Minnesota, [2] in order to gather research data on personalized recommendations. [3]
A recommender system (RecSys), or a recommendation system (sometimes replacing system with terms such as platform, engine, or algorithm), is a subclass of information filtering system that provides suggestions for items that are most pertinent to a particular user.
Netflix provided a training data set of 100,480,507 ratings that 480,189 users gave to 17,770 movies. Each training rating is a quadruplet of the form <user, movie, date of grade, grade>. The user and movie fields are integer IDs, while grades are from 1 to 5 stars. [3]
TasteDive (formerly named TasteKid) is an entertainment recommendation engine for films, TV shows, music, video games, books, people, places, and brands. It also has elements of a social media site; it allows users to connect with "tastebuds", people with like minded interests
The AOL.com video experience serves up the best video content from AOL and around the web, curating informative and entertaining snackable videos.
The Firefly website was created by Firefly Network, Inc.(originally known as Agents Inc.) [1] The company was founded in March 1995 by a group of engineers from MIT Media Lab and some business people from Harvard Business School, including Pattie Maes (Media Lab professor), Upendra Shardanand, Nick Grouf, Max Metral, David Waxman and Yezdi Lashkari. [2]
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The data about each title in a Movie Genome can also support an item-based recommendation engine [6] that recommends based on similarities between content items and users’ preferred “genes.” [7] By contrast, collaborative filtering is used to make recommendations based on statistical similarities in preferences between users.