What is normalized discounted cumulative gain (NCDG) at K (5 10 25) in AWS? Detailed Explanation

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Normalized Discounted Cumulative Gain (NDCG) is a widely used evaluation metric in information retrieval and recommendation systems to measure the quality and effectiveness of ranked lists. It assesses the relevance and ranking of items in a list.

NDCG at K, where K represents the position in the list, is computed by assigning higher weights to top-ranked items and gradually decreasing weights as the rank position gets lower. NDCG considers both relevancy and ranking order while disregarding irrelevant items.

NDCG is normalized to a value between 0 and 1, where 1 indicates a perfect ranking and 0 signifies a completely random or irrelevant ranking. It provides a comprehensive evaluation of how well a system ranks items and how useful the ranked list is to the user.

The value of K, such as 5, 10, or 25, determines the length of the evaluated list. For example, NDCG at 5 evaluates the ranking of the top 5 items, NDCG at 10 evaluates the top 10, and so on. The higher the value of K, the more comprehensive the evaluation becomes.

To calculate NDCG at K, the first step is to compute the Discounted Cumulative Gain (DCG). DCG is determined by assigning a discounted score to each item in the ranked list based on its relevance. Relevance scores are often predefined or collected through user feedback.

A discounted score is computed using a logarithmic function to give higher importance to items at the top of the list. The lower the position of an item, the greater its discount. This reflects the decreasing user satisfaction as they go further down the ranked list.

The DCG values for each item in the list are summed up to obtain the cumulative gain. NDCG is calculated by normalizing the DCG value with the maximum possible DCG, which is determined by an ideal ranking where all the relevant items appear at the top.

NDCG is an effective metric for evaluating recommendation systems and search engines as it provides a quantitative measure of the quality and utility of the ranked lists. It quantifies the trade-off between relevancy and ranking order, allowing for effective comparison and optimization of different algorithms or models.

In summary, NDCG at K is a normalized evaluation metric that assesses the quality of ranked lists. It considers both relevancy and ranking position, providing a comprehensive evaluation of recommendation systems and search engines.

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