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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 ...
YouTube has a pattern of recommending right-leaning and Christian videos, even to users who haven’t previously interacted with that kind of content, according to a recent study of the platform ...
A decontextualized algorithm uses unrelated information to sort results, for example, a flight-pricing algorithm that sorts results by alphabetical order would be biased in favor of American Airlines over United Airlines. [18]: 332 The opposite may also apply, in which results are evaluated in contexts different from which they are collected.
The paper on the algorithm was first published by Beate Commentz-Walter in 1979 through the Saarland University and typed by "R. Scherner". [1] The paper detailed two differing algorithms she claimed combined the idea of the Aho-Corasick and Boyer-Moore algorithms, which she called algorithms B and B1. The paper mostly focuses on algorithm B ...
This list of most-liked YouTube videos contains the top 30 videos with the most likes of all time, taken directly from the video page. The American video platform YouTube implemented a like and dislike button on these pages in March 2010, part of a major redesign of the site.
Best-first search is a class of search algorithms which explores a graph by expanding the most promising node chosen according to a specified rule.. Judea Pearl described best-first search as estimating the promise of node n by a "heuristic evaluation function () which, in general, may depend on the description of n, the description of the goal, the information gathered by the search up to ...
Relief algorithm: Selection of nearest hit, and nearest miss instance neighbors prior to scoring. Take a data set with n instances of p features, belonging to two known classes. Within the data set, each feature should be scaled to the interval [0 1] (binary data should remain as 0 and 1). The algorithm will be repeated m times.
In computer science, Thompson's construction algorithm, also called the McNaughton–Yamada–Thompson algorithm, [1] is a method of transforming a regular expression into an equivalent nondeterministic finite automaton (NFA). [2] This NFA can be used to match strings against the regular expression. This algorithm is credited to Ken Thompson.