Normalized Discounted Cumulative Gain (NDGC)NDCG) ist eine beliebte Metrik, die in dem Informationsretrieval and Empfehlungssystemen 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).
Die Berechnung von NDCG umfasst zwei Hauptschritte: Zuerst, computing the Kumulative Gewinn (CG), which sums the relevance scores of the retrieved items based on their ranks. The second step is applying a Discounting-Funktion, 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.
Um den CG zu normalisieren, verwendet NDCG einen Idealer diskontierter kumulativer Gewinn (IDCG), which represents the maximum possible gain for an ideal ranking von Elementen. Der endgültige NDCG-Wert wird berechnet als:
NDCG = (DCG) / (IDCG)
This normalization ensures that NDCG values range from 0 to 1, making it easier to compare the effectiveness of different Ranglisten-Algorithmen. 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 maschinellem Lernen models, to assess how well a system meets user expectations and retrieves pertinent information.