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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 .
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.
importance of these preferences to health behaviors, a recent meta-analysis of reinforcement contingency management (in which people are paid for improving health behaviors) found that the single most important determinant of effect size was whether behavior-contingent rewards
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.
A traditional snickerdoodle recipe includes unsalted butter, granulated sugar, eggs, all-purpose flour, cream of tartar, baking soda and salt.
Remember that guidelines are not set in stone — rather, they're good rules to follow. For instance, if you’re 30 years old and earn $75,000, you should try to have that much saved in your 401(k).
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]