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The GroupLens Research team, led by Brent Dahlen and Jon Herlocker, used this data set to jumpstart a new movie recommendation site, which they chose to call MovieLens. Since its inception, MovieLens has become a very visible research platform: its data findings have been featured in a detailed discussion in a New Yorker article by Malcolm ...
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.
Through this approach, dimensionality reduction methods are mostly used for improving robustness and accuracy of memory-based methods. Specifically, methods like singular value decomposition , principal component analysis , known as latent factor models, compress a user-item matrix into a low-dimensional representation in terms of latent factors.
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.
The AOL.com video experience serves up the best video content from AOL and around the web, curating informative and entertaining snackable videos.
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]
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