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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.
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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.
PageRank (PR) is an algorithm used by Google Search to rank web pages in their search engine results. It is named after both the term "web page" and co-founder Larry Page. PageRank is a way of measuring the importance of website pages. According to Google:
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.
The computational cost of the algorithm is a crucial factor since HITS and SALSA are computed at query time and can therefore significantly affect the response time of a search engine. This should be contrasted with query-independent algorithms like PageRank that can be computed off-line.
Commercial web search engines began using machine-learned ranking systems since the 2000s (decade). One of the first search engines to start using it was AltaVista (later its technology was acquired by Overture, and then Yahoo), which launched a gradient boosting-trained ranking function in April 2003. [51] [52]
In a 2015 interview, Google commented that RankBrain was the third most important factor in the ranking algorithm, after with links and content, [2] [3] out of about 200 ranking factors. [4] whose exact functions in the Google algorithm are not fully disclosed. As of 2015, "RankBrain was used for less than 15% of queries."