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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 ...
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]
Cross-platform open-source desktop search engine. Unmaintained since 2011-06-02 [9]. LGPL v2 [10] Terrier Search Engine: Linux, Mac OS X, Unix: Desktop search for Windows, Mac OS X (Tiger), Unix/Linux. MPL v1.1 [11] Tracker: Linux, Unix: Open-source desktop search tool for Unix/Linux GPL v2 [12] Tropes Zoom: Windows: Semantic Search Engine (no ...
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.
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 ...
It helps Google to process search results and provide more relevant search results for users. [2] In a 2015 interview, Google commented that RankBrain was the third most important factor in the ranking algorithm, after links and content, [ 2 ] [ 3 ] out of about 200 ranking factors [ 4 ] whose exact functions are not fully disclosed.
By limiting search results to only include matches where the words are within the specified maximum proximity, or distance, the search results are assumed to be of higher relevance than the matches where the words are scattered. Commercial internet search engines tend to produce too many matches (known as recall) for the average search query.