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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. [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]
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]
When two high school misfits and BFFs, Jodi (Justice) and Mindy (Sher), get pranked by the school's mean girls, they decide to fight back by spearheading an outcast uprising, with the help of ...
The AOL.com video experience serves up the best video content from AOL and around the web, curating informative and entertaining snackable videos.
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 user based top-N recommendation algorithm uses a similarity-based vector model to identify the k most similar users to an active user. After the k most similar users are found, their corresponding user-item matrices are aggregated to identify the set of items to be recommended.