Data Science Stack Exchange
Data Science Stack Exchange

Questions tagged [ndcg]

9 questions
3
votes
0 answers

Xgboost rank:ndcg learning per group or for all dataset

I'm trying to implement xgboost with an objective of rank:ndcg I want the target to be between 0-3. In my data for most of the groups, there is only 1 event per group which his target is not 0. I wonder if the model will learn different about this…
Dkova
  • 161
  • 4
3
votes
1 answer

Why is NDCG high even for wrongly ranked predictions?

The NDCG (Normalized Discounted Cumulative Gain) metric for ranking is defined as DCG/IDCG, where IDCG is the ideal DCG and is said to take values in [0, 1]. However, since the DCG will always be positive for any (positive) predicted scores, this…
Michael
  • 131
  • 1
2
votes
1 answer

How to explain a stable NDCG@K in extreme multilabel recommender model

I am working in a multilabel recommender project and I try to evaluate it as a ranking problem. I calculate recall@k and precision@k which both looks quite well. Recall increases and Precision decreases as I try higher K values, which is…
Tasos
  • 3,960
  • 5
  • 25
  • 54
2
votes
1 answer

How to calculate NDCG in recommendation system

This is a question about NDCG, which is a recommendation evaluation metric. The following are being used as evaluation indicators for recommendations. $$DCG = r_1 + \sum\limits_{i=2}^{N}\frac{r_i}{log_2i}$$ $$nDCG = \frac{DCG}{DCG_{perfect}}$$ The…
mhiro216
  • 23
  • 1
  • 3
2
votes
1 answer

What is the difference between nDCG and rank correlation methods?

When do we use one or the other? My use case: I want to evaluate a linear space to see how good retrieval results are. I have a set of data X (m x n) and some weights W (m x 1). I want to measure the nearest neighbour retrieval performance on W'X…
TyanTowers
  • 21
  • 1
1
vote
0 answers

How to use the position factor in known data as a feature in recommendation surfacing?

The problem is recommending stories on a website, just below each story based on how similar the stories are and some historic data based on what recommended stories were clicked or not clicked. So basically the available data looks like Referrer …
Della
  • 345
  • 1
  • 3
  • 9
1
vote
1 answer

Fix for NDCG Limitation

One limitation of NDCG and way to overcome the limitation (as mentioned in https://en.wikipedia.org/wiki/Discounted_cumulative_gain#Limitations) is "Normalized DCG does not penalize for missing documents in the result. For example, if a query…
jun
  • 125
  • 4
1
vote
0 answers

NDCG score is greater than 1

I'm solving a problem of ranking classes for each unique id based on the utilization quantity. I have 6 unique classes in the training and test data. My neural net mode predicts the utilization coressponding to each class. So if there are 10000 test…
iprof0214
  • 151
  • 1
  • 1
  • 6
0
votes
1 answer

How to estimate missing values when calculating NDCG

I would like to compare recommendations methods using NDCG metric on MovieLens dataset. In ranking problem, the goal is to rank items based on their relevance for user. Ranking models can be learned based on ratings matrix, where each user rates…
Michał Misiewicz
  • 1
  • 1