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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
Jinni was a website-based search engine and recommendation engine for movies, TV shows and short films.The service was powered by the Entertainment Genome, an approach to indexing titles based on attributes like mood, tone, plot, and structure.
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.
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]
Jake Gyllenhaal dealing with an aircraft engine falling into his bedroom and interacting with a giant rabbit named Frank – who tells him doomsday's afoot in 28 days – is all the answer you ...
If you’ve seen the classic enough times to memorize almost every line (guilty), you can still make “fetch” happen with these 20 movies like Mean Girls, available on Netflix, Hulu, Disney+ ...
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.
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