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The study noted that YouTube’s recommendation algorithm “drives 70% of all video views.” ...
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 ...
YouTube is an American social media and online video sharing platform owned by Google. YouTube was founded on February 14, 2005, by Steve Chen, Chad Hurley, and Jawed Karim, three former employees of PayPal. Headquartered in San Bruno, California, it is the second-most-visited website in the world, after Google Search.
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 ...
In Richards' view, it achieved this viewership by "situat(ing) a piece of music, and the listening experience, in the greater context of all media, all experience"—referring to the variety of content encountered through its "Up Next" algorithm. [38] Crediting YouTube's mobile accessibility, vast library size, visuality, portability, on-demand ...
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.
Ranking of query is one of the fundamental problems in information retrieval (IR), [1] the scientific/engineering discipline behind search engines. [2] Given a query q and a collection D of documents that match the query, the problem is to rank, that is, sort, the documents in D according to some criterion so that the "best" results appear early in the result list displayed to the user.
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.