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Successive paper sizes in the series (A1, A2, A3, etc.) are defined by halving the area of the preceding paper size and rounding down, so that the long side of A(n + 1) is the same length as the short side of An. Hence, each next size is nearly exactly half the area of the prior size. So, an A1 page can fit two A2 pages inside the same area.
The Canadian standard CAN2 9.60-M76 and its successor CAN/CGSB 9.60-94 "Paper Sizes for Correspondence" specified paper sizes P1 through P6, which are the U.S. paper sizes rounded to the nearest 5 mm. [32] All custom Canadian paper size standards were withdrawn in 2012.
[citation needed] A4 ("metric") paper is easier to obtain in the US than US letter can be had elsewhere. [citation needed]. The ISO 216:2007 is the current international standard for paper sizes, including writing papers and some types of printing papers. This standard describes the paper sizes under what the ISO calls the A, B, and C series ...
The study noted that YouTube’s recommendation algorithm “drives 70% of all video views.” ...
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 ...
Comparison of some newspaper sizes with metric paper sizes. Approximate nominal dimensions are in millimetres. A soldier reading Pravda, a broadsheet newspaper, in 1941. A broadsheet is the largest newspaper format and is characterized by long vertical pages, typically of 22.5 inches (57 cm) in height.
In the context of recommender systems a 2019 paper surveyed a small number of hand-picked publications applying deep learning or neural methods to the top-k recommendation problem, published in top conferences (SIGIR, KDD, WWW, RecSys, IJCAI), has shown that on average less than 40% of articles could be reproduced by the authors of the survey ...
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.