Q-Measure

Living reference work entry
DOI: https://doi.org/10.1007/978-1-4899-7993-3_80616-1
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Synonyms

None

Definition

Q-measure is a graded-relevance version of the well-known Average Precision. Let R denote the number of known relevant documents for a topic; let gainl denote the gain value for a document of relevance level l: for example, let it be 3 for a highly relevant document, 2 for a relevant document, and 1 for a partially relevant document. For a given ranked list of documents, let I(r) = 0 if the document at rank r is nonrelevant, and I(r) = 1 otherwise; then C(r) = k = 1rI(k): the number of relevant documents within top r. Note that Precision at r is given by C(r)∕r. Let g(r) = gainl if the document at rank r is l-relevant and let g(r) = 0 otherwise; let the cumulative gain be cg(r) = k = 1rg(k). Define an ideal ranked list for the topic by sorting the R relevant documents by relevance level; let cg(r) denote the cumulative gain at r for the ideal list. Then Q-measure is defined as:
$$\displaystyle\begin{array}{rcl} Q\mbox{ -}measure& =& \frac{1} {R}\sum...

Keywords

Average Cumulative Gain Shallowest Measurement Depth Ideal Ranked List Average Precision Users Stop 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Authors and Affiliations

  1. 1.Waseda UniversityTokyoJapan

Section editors and affiliations

  • Weiyi Meng
    • 1
  1. 1.Dept. of Computer ScienceState University of New York at BinghamtonBinghamtonUSA