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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 .
The relevance feedback information needs to be interpolated with the original query to improve retrieval performance, such as the well-known Rocchio algorithm. A performance metric which became popular around 2005 to measure the usefulness of a ranking algorithm based on the explicit relevance feedback is normalized discounted cumulative gain.
When applied to text classification using word vectors containing tf*idf weights to represent documents, the nearest centroid classifier is known as the Rocchio classifier because of its similarity to the Rocchio algorithm for relevance feedback.
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Many important concepts in information retrieval were developed as part of research on the SMART system, including the vector space model, relevance feedback, and Rocchio classification. Gerard Salton led the group that developed SMART. Other contributors included Mike Lesk.
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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]