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  2. Slope One - Wikipedia

    en.wikipedia.org/wiki/Slope_One

    Examples of binary item-based collaborative filtering include Amazon's item-to-item patented algorithm [12] which computes the cosine between binary vectors representing the purchases in a user-item matrix. Being arguably simpler than even Slope One, the Item-to-Item algorithm offers an interesting point of reference. Consider an example.

  3. The Computer Language Benchmarks Game - Wikipedia

    en.wikipedia.org/wiki/The_Computer_Language...

    The Computer Language Benchmarks Game (formerly called The Great Computer Language Shootout) is a free software project for comparing how a given subset of simple algorithms can be implemented in various popular programming languages. The project consists of: A set of very simple algorithmic problems

  4. 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.

  5. 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.

  6. Video recorder scheduling code - Wikipedia

    en.wikipedia.org/wiki/Video_recorder_scheduling_code

    The actual algorithms used to encode and decode the television guide values from and to their time representations were published in 1992, but only for six-digit codes or less. [1] [2] Source code for seven and eight digit codes was written in C and Perl and posted anonymously in 2003. [3]

  7. Matrix factorization (recommender systems) - Wikipedia

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

    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 challenge due to its effectiveness as reported by Simon Funk in his 2006 blog post, [ 2 ] where he shared his findings ...

  8. 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.

  9. Outline of computer programming - Wikipedia

    en.wikipedia.org/.../Outline_of_computer_programming

    Programming involves activities such as analysis, developing understanding, generating algorithms, verification of requirements of algorithms including their correctness and resources consumption, and implementation (commonly referred to as coding [1] [2]) of algorithms in a target programming language.