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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 .
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.
Rocchio proposed to judge manually some of the retrieved documents and use this feedback information to expand the query. Since collecting users' judgment can be challenging, only the first top retrieved documents are considered as relevant. This is the so called pseudo-relevance feedback (PRF). [6]
Rocchio Classification. In machine learning, a nearest centroid classifier or nearest prototype classifier is a classification model that assigns to observations the label of the class of training samples whose mean is closest to the observation.
Many models of communication include the idea that a sender encodes a message and uses a channel to transmit it to a receiver. Noise may distort the message along the way. The receiver then decodes the message and gives some form of feedback. [1] Models of communication simplify or represent the process of communication.
[1] [23] [12] Feedback means that the receiver responds by sending their own message back to the original sender. This makes the process more complicated since each participant acts both as sender and receiver. For many forms of communication, feedback is of vital importance, for example, to assess the effect of the communication on the audience.
Dan Sperber, who, with Deirdre Wilson, developed relevance theory. Relevance theory is a framework for understanding the interpretation of utterances.It was first proposed by Dan Sperber and Deirdre Wilson, and is used within cognitive linguistics and pragmatics.
The probabilistic relevance model [1] [2] was devised by Stephen E. Robertson and Karen Spärck Jones as a framework for probabilistic models to come. It is a formalism of information retrieval useful to derive ranking functions used by search engines and web search engines in order to rank matching documents according to their relevance to a given search query.