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  2. 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.

  3. 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 ...

  4. 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.

  5. Collaborative filtering - Wikipedia

    en.wikipedia.org/wiki/Collaborative_filtering

    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.

  6. Learning to rank - Wikipedia

    en.wikipedia.org/wiki/Learning_to_rank

    Training data is used by a learning algorithm to produce a ranking model which computes the relevance of documents for actual queries. Typically, users expect a search query to complete in a short time (such as a few hundred milliseconds for web search), which makes it impossible to evaluate a complex ranking model on each document in the ...

  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. Slope One - Wikipedia

    en.wikipedia.org/wiki/Slope_One

    Slope One is a family of algorithms used for collaborative filtering, introduced in a 2005 paper by Daniel Lemire and Anna Maclachlan. [1] Arguably, it is the simplest form of non-trivial item-based collaborative filtering based on ratings.

  9. Algorithmic curation - Wikipedia

    en.wikipedia.org/wiki/Algorithmic_curation

    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 .