Normalized Discounted Cumulative Gain (NDCG) is a popular metric used in information retrieval and recommendation systems 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).
The calculation of NDCG involves two main steps: first, computing the Cumulative Gain (CG), which sums the relevance scores of the retrieved items based on their ranks. The second step is applying a Discounting function, 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.
To normalize the CG, NDCG uses a Ideal Discounted Cumulative Gain (IDCG), which represents the maximum possible gain for an ideal ranking of items. The final NDCG score is calculated as:
NDCG = (DCG) / (IDCG)
This normalization ensures that NDCG values range from 0 to 1, making it easier to compare the effectiveness of different ranking algorithms. 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 machine learning models, to assess how well a system meets user expectations and retrieves pertinent information.