🎁 Hit Rate (HR)

Hit Rate (HR)#

The share of users for whom a relevant item appears in the top-N list.

Important

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What it is#

Hit Rate is the simplest top-N recommendation metric — it asks whether at least one relevant item appears in a user’s top-K list. Each user scores 1 if there’s any hit and 0 otherwise, and the metric is the average across users:

\[\text{HR@}K = \frac{\#\{\text{users with} \ge 1 \text{ relevant item in top } K\}}{|U|}.\]

What it captures#

HR measures coverage of intent at the coarsest level — did we surface something the user wanted? — which is exactly right for feeds, “you might also like” rows, and any setting where a single good hit is a win. It is intuitive and easy to explain to stakeholders.

Its limits#

HR is binary and position-blind — it doesn’t care where in the list the hit landed or how many relevant items were found, so a hit at rank 1 and a hit at rank 10 score the same. It also rises mechanically with K, so always report the cutoff (Hit@5 vs Hit@10) and pair it with a ranking metric.

Theme: Recommender Systems · All terminology

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Mind map — connected ideas

NDCG (Normalized Discounted Cumulative Gain) · Mean Average Precision (MAP) · DCG (Discounted Cumulative Gain) · Average Precision (AP) · Relevance in Recommender Systems · Recall

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More in Recommender Systems

Catalog Coverage · Cosine Similarity of Item Features · Diminishing Utility · Diversity (in Recommender Systems) · Dominating in Recommender Systems · Genre Overlap · Intra-List Diversity (ILD) · Item Coverage · Jaccard index · Novelty (in Recommender Systems) · Relevance in Recommender Systems · Self-Information of Popularity · User Coverage