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  2. Relevance feedback - Wikipedia

    en.wikipedia.org/wiki/Relevance_feedback

    Relevance feedback is a feature of some information retrieval systems. The idea behind relevance feedback is to take the results that are initially returned from a given query, to gather user feedback, and to use information about whether or not those results are relevant to perform a new query. We can usefully distinguish between three types ...

  3. Query expansion - Wikipedia

    en.wikipedia.org/wiki/Query_expansion

    Machine learning based query term weight and synonym analyzer for query expansion. LucQE - open-source, Java. Provides a framework along with several implementations that allow to perform query expansion with the use of Apache Lucene. Xapian is an open-source search library which includes support for query expansion; ReQue open-source, Python ...

  4. Rocchio algorithm - Wikipedia

    en.wikipedia.org/wiki/Rocchio_algorithm

    The Rocchio algorithm is based on a method of relevance feedback found in information retrieval systems which stemmed from the SMART Information Retrieval System developed between 1960 and 1964. Like many other retrieval systems, the Rocchio algorithm was developed using the vector space model .

  5. Extended Boolean model - Wikipedia

    en.wikipedia.org/wiki/Extended_Boolean_model

    Further, research has shown effectiveness improves relative to that for Boolean query processing. Other research has shown that relevance feedback and query expansion can be integrated with extended Boolean query processing.

  6. Concept search - Wikipedia

    en.wikipedia.org/wiki/Concept_search

    Relevance feedback has been shown to be very effective at improving the relevance of results. [21] A concept search decreases the risk of missing important result items because all of the items that are related to the concepts in the query will be returned whether or not they contain the same words used in the query.

  7. Evaluation measures (information retrieval) - Wikipedia

    en.wikipedia.org/wiki/Evaluation_measures...

    The most important factor in determining a system's effectiveness for users is the overall relevance of results retrieved in response to a query. [1] The success of an IR system may be judged by a range of criteria including relevance, speed, user satisfaction, usability, efficiency and reliability. [2]

  8. Trump bristles at Musk’s rocketing profile as Democrats play ...

    www.aol.com/news/trump-bristles-musk-rocketing...

    Whether Elon Musk is the real “president,” merely the “prime minister” or just Donald Trump’s multibillionaire enforcer, he’s carving out an unprecedented role that could raise ...

  9. 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.