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  2. YouTube's algorithm more likely to recommend users ... - AOL

    www.aol.com/news/youtube-algorithm-more-likely...

    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 ...

  3. Recommender system - Wikipedia

    en.wikipedia.org/wiki/Recommender_system

    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.

  4. Netflix Prize - Wikipedia

    en.wikipedia.org/wiki/Netflix_Prize

    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.

  5. Collaborative filtering - Wikipedia

    en.wikipedia.org/wiki/Collaborative_filtering

    In order to make appropriate recommendations for a new user, the system must first learn the user's preferences by analysing past voting or rating activities. The collaborative filtering system requires a substantial number of users to rate a new item before that item can be recommended.

  6. Instagram and Twitch roll out new TikTok-like short-form ...

    www.aol.com/news/instagram-twitch-roll-tiktok...

    Instagram and Twitch rolled out new features this week that would boost short-form video content in ways reminiscent of TikTok’s discovery algorithm.

  7. Matrix factorization (recommender systems) - Wikipedia

    en.wikipedia.org/wiki/Matrix_factorization...

    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]

  8. Rabbit Hole (podcast) - Wikipedia

    en.wikipedia.org/wiki/Rabbit_Hole_(podcast)

    YouTube's content recommendation algorithm is designed to keep the user engaged as long as possible, which Roose calls the "rabbit hole effect". [5] The podcast features interviews with a variety of people involved with YouTube and the "rabbit hole effect". [6] For instance, in episode four Roose interviews Susan Wojcicki—the CEO of YouTube. [2]

  9. YouTube moderation - Wikipedia

    en.wikipedia.org/wiki/YouTube_moderation

    YouTube has faced criticism over aspects of its operations, [1] its recommendation algorithms perpetuating videos that promote conspiracy theories and falsehoods, [2] hosting videos ostensibly targeting children but containing violent or sexually suggestive content involving popular characters, [3] videos of minors attracting pedophilic ...