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The study noted that YouTube’s recommendation algorithm “drives 70% of all video views.” ... “YouTube’s recommendation system is trained to raise high-quality content on the home page ...
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.
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.
Algorithmic radicalization is the concept that recommender algorithms on popular social media sites such as YouTube and Facebook drive users toward progressively more extreme content over time, leading to them developing radicalized extremist political views. Algorithms record user interactions, from likes/dislikes to amount of time spent on ...
The user based top-N recommendation algorithm uses a similarity-based vector model to identify the k most similar users to an active user. After the k most similar users are found, their corresponding user-item matrices are aggregated to identify the set of items to be recommended.
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.
Algorithmic curation is the selection of online media by recommendation algorithms and personalized searches. Examples include search engine and social media products [ 1 ] such as the Twitter feed , Facebook 's News Feed , and the Google Personalized Search .
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]