Catalog Coverage is an important evaluation metric for recommendation diversity and novelty tasks in the natural language processing field. Coverage measures the proportion of items in the item catalog that a recommendation system can cover, reflecting the breadth of the recommendation distribution and its long-tail friendliness. It is typically reported as a percentage or a score from 0 to 1. This metric is widely used in related benchmarks and papers, and can effectively reflect model performance on this dimension. Higher values are better. It helps researchers and engineers quantify actual performance differences of models in recommendation diversity and novelty scenarios, facilitating horizontal comparisons across different methods or model versions.