Coverage#
The share of outcomes that fall within predicted intervals.
Important
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What it is#
Coverage measures how much of the catalog a recommender actually uses — the share of available items it is able to, or chooses to, recommend. A system can be accurate yet only ever surface a handful of popular items, ignoring the rest.
Two flavors#
Prediction coverage is the fraction of items for which the model can make a prediction at all; catalog coverage is the fraction of items that actually appear in the recommendation lists users see. The latter is the usual beyond-accuracy target.
Why it matters#
High coverage means the long tail gets exposure and the catalog isn’t wasted — countering popularity bias. A limitation of plain coverage: it counts an item shown once the same as one shown thousands of times, which is why Gini and entropy refine it to capture how evenly exposure is spread.
Theme: Model Evaluation & Uncertainty · All terminology
Hint
Mind map — connected ideas
Novelty (in Recommender Systems) · Diversity (in Recommender Systems) · Intra-List Diversity (ILD) · Relevance in Recommender Systems · Hit Rate (HR) · Mean Average Precision (MAP)
Hint
More in Model Evaluation & Uncertainty
Average Absolute Error (AAE) · Baseline Heuristics · Bootstrap · Bootstrap Confidence Intervals (CIs) · Cramér’s V · DeLong’s Test · KS Statistic (Kolmogorov–Smirnov Statistic) · Likelihood Ratio (LR) · Mann–Whitney U Test (also called the Wilcoxon rank-sum test) · MASE (Mean Absolute Scaled Error) · Mean Absolute Error (MAE) · Mean Absolute Percentage Error (MAPE) · Mean Squared Error (MSE) · Relative accuracy
See also
Source article Adapted (context, re-expressed) in our own words from: Coverage (insightful-data-lab.com).