Unverified50% confidenceFactExact time
Reciprocal Rank@k is defined as the reciprocal of the rank of the first relevant result: ranking first gives 1 point, ranking third gives 1/3 point, and having none at all gives 0 points.
1
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50%
Confidence
Long-term
Relevance
10/12/2026
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Related Claims
Unverified盲测评分系统采用第一名3分、第二名2分、第三名1分的计分方式65% similarUnverifiedThe first metric is Effective Rank, which measures in a continuous manner how many directions the Jacobian matrix truly acts upon.64% similarVerifiedThe Reciprocal Rank Fusion (RRF) algorithm was proposed by Cormack et al. in 2009, with a core formula of Score = Σ 1/(k + rank_i), where k typically takes the value 60 as a smoothing constant.63% similarUnverifiedThe author uses a weighted scoring scheme: in the outcome dimension, Pass scores 1.0, Partial scores 0.5, and Fail scores 0; severity weights are 4 for Critical, 3 for High, 2 for Medium, and 1 for Low.60% similarUnverifiedRecall@K measures whether relevant documents appear in the top K results, while MRR measures the ranking position of the first relevant result.60% similar
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