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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] MovieLens was created in 1997 by GroupLens Research, a ...
The Netflix Prize was an open competition for the best collaborative filtering algorithm to predict user ratings for films, based on previous ratings without any other information about the users or films, i.e. without the users being identified except by numbers assigned for the contest. The competition was held by Netflix, a video streaming ...
A recommender system, 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. [1] [2] [3] Recommender systems are particularly useful when an individual needs to ...
GroupLens Research is a human–computer interaction research lab in the Department of Computer Science and Engineering at the University of Minnesota, Twin Cities specializing in recommender systems and online communities. GroupLens also works with mobile and ubiquitous technologies, digital libraries, and local geographic information systems .
v. t. e. 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] This family of methods became widely known during the Netflix prize challenge due ...
Collaborative filtering (CF) is a technique used by recommender systems. [1] Collaborative filtering has two senses, a narrow one and a more general one. [2]In the newer, narrower sense, collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating).
Lazy learning. In machine learning, lazy learning is a learning method in which generalization of the training data is, in theory, delayed until a query is made to the system, as opposed to eager learning, where the system tries to generalize the training data before receiving queries. [1]
Entree Chicago Recommendation Dataset Record of user interactions with Entree Chicago recommendation system. Details of each users usage of the app are recorded in detail. 50,672 Text Regression, recommendation 2000 [471] R. Burke Insurance Company Benchmark (COIL 2000) Information on customers of an insurance company.