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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]
FREE Resources: 3 articles every 2 weeks (Register and Read Program, archived journals). Also, early journals (prior to 1923 in US, 1870 elsewhere) free, no registry necessary. Free and Subscription JSTOR [89] Jurn: Multidisciplinary Jurn is a free-to-use online search tool for finding and downloading free full-text scholarly works.
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
Google Scholar is a freely accessible web search engine that indexes the full text or metadata of scholarly literature across an array of publishing formats and disciplines. . Released in beta in November 2004, the Google Scholar index includes peer-reviewed online academic journals and books, conference papers, theses and dissertations, preprints, abstracts, technical reports, and other ...
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.
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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 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 ...