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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 of feedback: explicit feedback, implicit feedback, and blind or "pseudo ...
Its underlying assumption is that most users have a general conception of which documents should be denoted as relevant or irrelevant. [1] Therefore, the user's search query is revised to include an arbitrary percentage of relevant and irrelevant documents as a means of increasing the search engine 's recall , and possibly the precision as well.
The global interpretation assumes that there exist some fixed set of underlying topics derived from inter-document similarity. These global clusters or their representatives can then be used to relate relevance of two documents (e.g. two documents in the same cluster should both be relevant to the same request). Methods in this spirit include:
Contextual search is a form of optimizing web-based search results based on context provided by the user and the computer being used to enter the query. [1] Contextual search services differ from current search engines based on traditional information retrieval that return lists of documents based on their relevance to the query.
So it can be looked at as the probability that a relevant document is retrieved by the query. It is trivial to achieve recall of 100% by returning all documents in response to any query. Therefore, recall alone is not enough but one needs to measure the number of non-relevant documents also, for example by computing the precision.
Information retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an information need. The information need can be specified in the form of a search query. In the case of document retrieval, queries can be based on full-text or other
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.
In the algorithmic ranking model that search engines used in the past, relevance of a site is determined after analyzing the text and content on the page and link structure of the document. In contrast, search results with social search highlight content that was created or touched by other users who are in the Social Graph of the person ...