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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]
MovieLens bases its recommendations on input provided by users of the website, such as movie ratings. [2] The site uses a variety of recommendation algorithms, including collaborative filtering algorithms such as item-item, [10] user-user, and regularized SVD. [11]
The cold start problem is a well known and well researched problem for recommender systems.Recommender systems form a specific type of information filtering (IF) technique that attempts to present information items (e-commerce, films, music, books, news, images, web pages) that are likely of interest to the user.
A user expresses his or her preferences by rating items (e.g. books, movies, or music recordings) of the system. These ratings can be viewed as an approximate representation of the user's interest in the corresponding domain. The system matches this user's ratings against other users' and finds the people with most "similar" tastes.
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Matrix factorization is a class of collaborative filtering algorithms used in recommender systems.Matrix factorization algorithms work by decomposing the user-item interaction matrix into the product of two lower dimensionality rectangular matrices. [1]
The Netflix Prize was an open competition for the best collaborative filtering algorithm to predict user ratings for films, based on previous ratings. The prize would be awarded to the team achieving over 10% improvement over Netflix's own Cinematch algorithm. The team "Gravity" was the front runner during January—May 2007. [2]