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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.
The information retrieval community has emphasized the use of test collections and benchmark tasks to measure topical relevance, starting with the Cranfield Experiments of the early 1960s and culminating in the TREC evaluations that continue to this day as the main evaluation framework for information retrieval research.
Though there are benefits to ranking documents as not-relevant, a relevant document ranking will result in more precise documents being made available to the user. Therefore, traditional values for the algorithm's weights ( a {\displaystyle a} , b {\displaystyle b} , c {\displaystyle c} ) in Rocchio classification are typically around a ...
Indexing and classification methods to assist with information retrieval have a long history dating back to the earliest libraries and collections however systematic evaluation of their effectiveness began in earnest in the 1950s with the rapid expansion in research production across military, government and education and the introduction of computerised catalogues.
In pattern recognition, information retrieval, object detection and classification (machine learning), precision and recall are performance metrics that apply to data retrieved from a collection, corpus or sample space. Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances. Written ...
The College Football Playoff cake is getting close to baked, which means much of the angst and anger of the past few weeks over hypothetical and projected scenarios have proved a waste of time.
A Dutch court convicted five men Tuesday for their part in last month's violence against Israeli soccer fans in Amsterdam that shocked the world and sparked accusations of antisemitism. The ...
This is the so called pseudo-relevance feedback (PRF). [6] Pseudo-relevance feedback is efficient in average but can damage results for some queries, [7] especially difficult ones since the top retrieved documents are probably non-relevant. Pseudo-relevant documents are used to find expansion candidate terms that co-occur with many query terms. [8]