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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 ( integer ) stars.
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 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 ...
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The motivation for collaborative filtering comes from the idea that people often get the best recommendations from someone with tastes similar to themselves. [ citation needed ] Collaborative filtering encompasses techniques for matching people with similar interests and making recommendations on this basis.