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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.
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.
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
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.
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 ( integer ) stars.
The Jinni service included semantic search, [1] a meaning-based approach to interpreting queries by identifying concepts within the content, rather than keywords. The search engine served as a video discovery tool focusing on user tastes, including mood, plot, and other parameters, with options to browse and refine using additional terms, e.g., “action in a future dystopia” or “Beautiful ...
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.