Métrique de Gain Cumulatif Discounté Normalisé (NDCG)
La Gain Cumulé Normalisé Discret (NDCG) (NDCG) metric is widely used in la récupération d'informations and systèmes de recommandation to evaluate the effectiveness of algorithms in ranking items according to their relevance to a user’s query. NDCG takes into account both the position of an item in the ranked list and the relevance of that item, making it particularly useful when the items have varying levels of importance.
NDCG is calculated in two main steps: first, the Discounted Cumulative Gain (DCG) is computed, which sums the relevance scores of the retrieved items, discounted by their rank position. The formula for DCG at rank p is given by:
DCG_p = rel_1 + Σ (rel_i / log2(i + 1)), for i = 2 to p
where rel_i is the score de pertinence of the item at rank i.
Ensuite, le NDCG normalise le score DCG en le divisant par le DCG idéal (IDCG), qui est le DCG de la liste idéale classée (le meilleur classement possible des éléments en fonction de leur pertinence). Cette normalisation permet d’exprimer le NDCG sur une échelle de 0 à 1, où un score de 1 indique un classement parfait des éléments.
NDCG is particularly valuable in scenarios where the relevance of results is not binary (relevant or not) but graded, such as in moteurs de recherche or recommendation systems. By utilizing NDCG, developers can gain insights into how well their algorithms perform in providing relevant results to users, thereby improving user satisfaction and experience.