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The PAPRIKA method pertains to value models for ranking particular alternatives that are known to decision-makers (e.g. as in the job candidates example above) and also to models for ranking potentially all hypothetically possible alternatives in a pool that is changing over time (e.g. patients presenting for medical care).
Learning to rank [1] or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems. [2] Training data may, for example, consist of lists of items with some partial order specified between items in ...
The ranking SVM algorithm is a learning retrieval function that employs pairwise ranking methods to adaptively sort results based on how 'relevant' they are for a specific query. The ranking SVM function uses a mapping function to describe the match between a search query and the features of each of the possible results.
Query-dependent methods attempt to measure the degree to which a page matches a specific query, independent of the importance of the page. Query-dependent ranking is usually based on heuristics that consider the number and locations of matches of the various query words on the page itself, in the URL or in any anchor text referring to the page.
The Borda count has been proposed as a rank aggregation method in information retrieval, in which documents are ranked according to multiple criteria and the resulting rankings are then combined into a composite ranking. In this method, the ranking criteria are treated as voters, and the aggregate ranking is the result of applying the Borda ...
The Kendall tau distance between two rankings is the number of pairs that are in different order in the two rankings. For example, the Kendall tau distance between 0 3 1 6 2 5 4 and 1 0 3 6 4 2 5 is four because the pairs 0-1, 3-1, 2-4, 5-4 are in different order in the two rankings, but all other pairs are in the same order. [1]
The Jenks optimization method, also called the Jenks natural breaks classification method, is a data clustering method designed to determine the best arrangement of values into different classes. This is done by seeking to minimize each class's average deviation from the class mean, while maximizing each class's deviation from the means of the ...
The model is named after Ralph A. Bradley and Milton E. Terry, [3] who presented it in 1952, [4] although it had already been studied by Ernst Zermelo in the 1920s. [1] [5] [6] Applications of the model include the ranking of competitors in sports, chess, and other competitions, [7] the ranking of products in paired comparison surveys of consumer choice, analysis of dominance hierarchies ...