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Recommendation Coverage Calculator

Work out recommendation coverage instantly with clear inputs, formula shown and shareable results.

About Recommendation Coverage Calculator

Catalogue coverage is the share of items a recommender ever surfaces. Popularity-biased models score well on accuracy while recommending a tiny head of the catalogue, which starves the long tail of the impressions it needs to accumulate interaction data — a feedback loop that makes coverage fall further over time. Coverage is therefore a health metric that accuracy alone cannot reveal.

Formula

Coverage

coverage = distinct items recommended / catalogue size; impressions per covered item = slots served / distinct items

Worked example

With the default values below, Recommendation Coverage Calculator returns Catalogue coverage of 15.00%. Change any input above and every figure here is recalculated in your browser.

Inputs used

Items in the catalogueminimum 1
120,000
Distinct items ever recommendedminimum 0
18,000
Recommendation slots servedminimum 1
5,000,000

Results

Catalogue coverage
15.00%
Items never surfaced
102,000
Average impressions per surfaced item
277.8
Catalogue never shown
85.00%

Step by step

  1. 1Values used
  2. 2Coverage
  3. 3Catalogue coverage= 15.00
  4. 4Items never surfaced= 102,000
  5. 5Average impressions per surfaced item= 277.8
  6. 6Catalogue never shown= 85.00

Assumptions and limits

  • The result is derived only from the 3 inputs listed above. Anything not among them is not part of the calculation.
  • In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
  • Figures are rounded for display to 2 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.

How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.

Frequently Asked Questions

What coverage should I target?

It depends on the business, but below roughly 20 percent most of the catalogue is commercially dead. Track the trend rather than an absolute target.

How is coverage improved?

Add exploration such as epsilon-greedy or Thompson sampling to the ranker, apply popularity debiasing in training, and reserve slots for cold-start and long-tail items.

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