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MovieLens is a web-based recommender system and virtual community that recommends movies for its users to watch, based on their film preferences using collaborative filtering of members' movie ratings and movie reviews. It contains about 11 million ratings for about 8500 movies. [1]
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]
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
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.
If you’ve seen the classic enough times to memorize almost every line (guilty), you can still make “fetch” happen with these 20 movies like Mean Girls, available on Netflix, Hulu, Disney+ ...
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.
Zen (recommendation system) This page was last edited on 1 October 2023, at 17:28 (UTC). Text is available under the Creative Commons Attribution ...