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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.
Cumulative Gain is the sum of the graded relevance values of all results in a search result list. CG does not take into account the rank (position) of a result in the result list. The CG at a particular rank position is defined as: = = Where is the graded relevance of the result at position . The value computed with the CG function is ...
TrustRank is an algorithm that conducts link analysis to separate useful webpages from spam and helps search engine rank pages in SERPs (Search Engine Results Pages). It is semi-automated process which means that it needs some human assistance in order to function properly.
Recently they have also sponsored a machine-learned ranking competition "Internet Mathematics 2009" [56] based on their own search engine's production data. Yahoo has announced a similar competition in 2010. [57] As of 2008, Google's Peter Norvig denied that their search engine exclusively relies on machine-learned ranking. [58]
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.
Even misspelled words, non-words, and words with numbers in them are indexed and stemmed in this way. By adding different forms of the same word to the indexed search query, stemming is a standard method search engines use to aggressively garner more search results to then run a bunch of page-ranking rules against.
Most search engines employ methods to rank the results to provide the "best" results first. How a search engine decides which pages are the best matches, and what order the results should be shown in, varies widely from one engine to another. [35] The methods also change over time as Internet usage changes and new techniques evolve.
Many search engines incorporate an inverted index when evaluating a search query to quickly locate documents containing the words in a query and then rank these documents by relevance. Because the inverted index stores a list of the documents containing each word, the search engine can use direct access to find the documents associated with ...