Ganancia acumulada descontada normalizada (NDCG) es una métrica popular utilizada en recuperación de información and sistemas de recomendación to evaluate the quality of ranked lists. NDCG takes into account the position of relevant documents in the ranked list, emphasizing the importance of higher-ranked items. The metric is especially useful in scenarios where the relevance of items is not binary but graded (e.g., on a scale from 0 to 3).
El cálculo de NDCG implica dos pasos principales: primero, computing the Ganancia Cumulativa (CG), which sums the relevance scores of the retrieved items based on their ranks. The second step is applying a Función de Descuento, often logarithmic, to reduce the weight of lower-ranked items, reflecting the principle that users are more likely to engage with items presented earlier in the list.
Para normalizar la CG, NDCG utiliza una Ganancia Cumulativa Descontada Ideal (IDCG), which represents the maximum possible gain for an ideal ranking de elementos. La puntuación final de NDCG se calcula como:
NDCG = (DCG) / (IDCG)
This normalization ensures that NDCG values range from 0 to 1, making it easier to compare the effectiveness of different algoritmos de clasificación. A higher NDCG score indicates better retrieval performance, as it means that relevant items are ranked higher.
NDCG is widely used in various applications, including search engines, recommendation systems, and aprendizaje automático models, to assess how well a system meets user expectations and retrieves pertinent information.