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Ranking Discount Shared Item Contribution Tool

Compute ranking discount shared item contribution from explicit bounded inputs.

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Description

Return one/log2(rank+1) when relevant and zero otherwise, requiring one-based integer rank from one through one million.

A similarity term depends on the comparison universe, missing-value policy, treatment of repeated features, and normalization. A score can also have a caller-selected value when its mathematical denominator vanishes. Those conventions matter before interpreting a threshold or comparing methods.

Return one/log2(rank+1) when relevant and zero otherwise, requiring one-based integer rank from one through one million.

When to use this tool

Use Ranking Discount Shared Item Contribution when you need this particular comparison term, normalization, domain condition, or empty-result policy in a larger matching workflow.

How to use the tool and how it works

  1. Enter rank, relevant. Use the units and conventions below.
  2. Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
  3. Read contribution. Use the output convention below.

Return one/log2(rank+1) when relevant and zero otherwise, requiring one-based integer rank from one through one million.1

Input fieldMeaning and unitsAccepted input
rankOne-based ranking position.Required. Safe integer; minimum 1; maximum 1000000.
relevantRelevantRequired. Boolean.
Output fieldInterpretationReturned shape
contributionOne term under the stated formula, not necessarily a complete statistic.Required. Finite number.
json
{
  "input": {
    "rank": 1,
    "relevant": true
  },
  "output": {
    "contribution": 1
  }
}

Limitations and assumptions

  • This is one binary-relevance discounted contribution, not a complete DCG or normalized ranking score. Relevance and ranking positions are supplied rather than judged or generated.
  • The field tables state structural bounds; the mechanics also specify cross-field constraints. Malformed inputs and unsupported domains cause errors. A successful false flag, empty branch value, or zero placeholder has the meaning stated above and is distinct from an input error.
  • Numeric calculations use binary floating point unless exact decimal-integer arithmetic is explicitly stated. No hidden measurement tolerance or decimal rounding is added; nonfinite or out-of-contract results are rejected. Signed zero is exported as zero.

Alternative or Complementary methods

Compare methods only after aligning feature definitions, missingness, empty policies, multiplicities, and normalizers. Use a complete comparison when the tool returns only one contribution or count check.

References

  1. Calculation and input contract: similarity/ranking-discount-shared-item-contribution — tool.io

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