Search results
Results From The WOW.Com Content Network
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.
A search engine called "RankDex" from IDD Information Services, designed by Robin Li in 1996, developed a strategy for site-scoring and page-ranking. [15] Li referred to his search mechanism as "link analysis," which involved ranking the popularity of a web site based on how many other sites had linked to it. [16] RankDex, the first search ...
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 ...
The probabilistic relevance model [1] [2] was devised by Stephen E. Robertson and Karen Spärck Jones as a framework for probabilistic models to come. It is a formalism of information retrieval useful to derive ranking functions used by search engines and web search engines in order to rank matching documents according to their relevance to a given search query.
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.
Indexing and classification methods to assist with information retrieval have a long history dating back to the earliest libraries and collections however systematic evaluation of their effectiveness began in earnest in the 1950s with the rapid expansion in research production across military, government and education and the introduction of computerised catalogues.
A number of metrics are available to marketers interested in search engine optimization. Search engines and software creating such metrics all use their own crawled data to derive at a numeric conclusion on a website's organic search potential. Since these metrics can be manipulated, they can never be completely reliable for accurate and ...