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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]
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]
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.
The Mendeley research catalog is a crowdsourced database of research documents. Researchers have uploaded nearly 100M documents into the catalog with additional contributions coming directly from subject repositories like Pubmed Central and Arxiv.org or web crawls. Free Mendeley [99] Merck Index: Chemistry, biology, pharmacology: Also available ...
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.
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Site members may follow a research interest, in addition to following other individual members. [10] It has a blogging feature for users to write short reviews on peer-reviewed articles. [ 10 ] ResearchGate indexes self-published information on user profiles to suggest members to connect with others who have similar interests. [ 3 ]