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  2. Ranking (information retrieval) - Wikipedia

    en.wikipedia.org/wiki/Ranking_(information...

    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.

  3. Discounted cumulative gain - Wikipedia

    en.wikipedia.org/wiki/Discounted_cumulative_gain

    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.

  4. Category:Internet search algorithms - Wikipedia

    en.wikipedia.org/wiki/Category:Internet_search...

    Download as PDF; Printable version; In other projects ... Help. Algorithms used in web search engines. See Category:Ranking functions for ranking algorithms suitable ...

  5. List of search engines - Wikipedia

    en.wikipedia.org/wiki/List_of_search_engines

    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 ...

  6. Learning to rank - Wikipedia

    en.wikipedia.org/wiki/Learning_to_rank

    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.

  7. Okapi BM25 - Wikipedia

    en.wikipedia.org/wiki/Okapi_BM25

    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.

  8. TrustRank - Wikipedia

    en.wikipedia.org/wiki/TrustRank

    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 ...

  9. Timeline of web search engines - Wikipedia

    en.wikipedia.org/wiki/Timeline_of_web_search_engines

    Robin Li developed the RankDex site-scoring algorithm for search engines results page ranking [23] [24] [25] and received a US patent for the technology. [26] It was the first search engine that used hyperlinks to measure the quality of websites it was indexing, [ 27 ] predating the very similar algorithm patent filed by Google two years later ...