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Recommender systems. 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 ...
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).
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.
Recommender systems. 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 ...
Netflix Prize. ACM Conference on Recommender Systems. v. t. e. Cold start is a potential problem in computer-based information systems which involves a degree of automated data modelling. Specifically, it concerns the issue that the system cannot draw any inferences for users or items about which it has not yet gathered sufficient information.
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 ...
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 ...
Connect schools with resources, volunteers from the community to support schools in upkeep of gardens in summer months and in other projects. Make recommendations on non-toxic pesticides and the ...
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related to: recommendation system projects