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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. Algorithmic radicalization - Wikipedia

    en.wikipedia.org/wiki/Algorithmic_radicalization

    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 ...

  5. Cold start (recommender systems) - Wikipedia

    en.wikipedia.org/wiki/Cold_start_(recommender...

    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.

  6. CatBoost - Wikipedia

    en.wikipedia.org/wiki/Catboost

    It provides a gradient boosting framework which, among other features, attempts to solve for categorical features using a permutation-driven alternative to the classical algorithm. [7] It works on Linux , Windows , macOS , and is available in Python , [ 8 ] R , [ 9 ] and models built using CatBoost can be used for predictions in C++ , Java ...

  7. Collaborative filtering - Wikipedia

    en.wikipedia.org/wiki/Collaborative_filtering

    Collaborative filtering algorithms often require (1) users' active participation, (2) an easy way to represent users' interests, and (3) algorithms that are able to match people with similar interests. Typically, the workflow of a collaborative filtering system is:

  8. Alt-right pipeline - Wikipedia

    en.wikipedia.org/wiki/Alt-right_pipeline

    The alt-right pipeline (also called the alt-right rabbit hole) is a proposed conceptual model regarding internet radicalization toward the alt-right movement. It describes a phenomenon in which consuming provocative right-wing political content, such as antifeminist or anti-SJW ideas, gradually increases exposure to the alt-right or similar far-right politics.

  9. 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.