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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 stars. [3]
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.
A 2012 paper published in The International Information & Library Review conducted a survey with 160 respondents and reported that out of those respondents using social networking "for academic purposes", Facebook and ResearchGate were the most popular at the University of Delhi, but also "a majority of respondents said using SNSs [Social ...
The AOL.com video experience serves up the best video content from AOL and around the web, curating informative and entertaining snackable videos.
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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 ...