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




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...


Average Cumulative Gain Shallowest Measurement Depth Ideal Ranked List Average Precision Users Stop 
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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