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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 nDCG values for all queries can be averaged to obtain a measure of the average performance of a search engine's ranking algorithm. Note that in a perfect ranking algorithm, the will be the same as the producing an nDCG of 1.0. All nDCG calculations are then relative values on the interval 0.0 to 1.0 and so are cross-query comparable.
A possible architecture of a machine-learned search engine. Ranking is a central part of many information retrieval problems, such as document retrieval, collaborative filtering, sentiment analysis, and online advertising. A possible architecture of a machine-learned search engine is shown in the accompanying figure.
In information retrieval, Okapi BM25 (BM is an abbreviation of best matching) is a ranking function used by search engines to estimate the relevance of documents to a given search query. It is based on the probabilistic retrieval framework developed in the 1970s and 1980s by Stephen E. Robertson , Karen Spärck Jones , and others.
Evaluation measures for an information retrieval (IR) system assess how well an index, search engine, or database returns results from a collection of resources that satisfy a user's query. They are therefore fundamental to the success of information systems and digital platforms.
TIOBE index is sensitive to the ranking policy of the search engines on which it is based. For instance, in April 2004 Google performed a cleanup action to get rid of unfair attempts to promote the search rank. As a consequence, there was a large drop for languages such as Javascript and C++, yet these languages have stayed at the top of the ...
Today, this algorithm is a part of major web search engines like Yahoo! and Google. [2] One of the most important factors that help web search engine determine the quality of a web page when returning results are backlinks. Search engines take a number and quality of backlinks into consideration when assigning a place to a certain web page in ...
It considers the relevance of each document only in terms of how much new information it brings given the previous results. [13] In some cases, a query may have an ambiguous interpretation, or a variety of potential responses. Providing a diversity of results can be a consideration when evaluating the utility of a result set. [14]