Skip to content

[DOCS] Fixed NDCG formula issues #22090

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
yueguoguo wants to merge 3 commits into from
Closed

[DOCS] Fixed NDCG formula issues #22090

Changes from 2 commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
b3e00ed
[DOCS] Fixed NDCG formula issues
yueguoguo Aug 13, 2018
d0f814f
[DOC] Update NDCG formula and wiki link
yueguoguo Aug 20, 2018
f87cf61
[DOCS] Restore "-1" used in the upper bound
yueguoguo Aug 20, 2018
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
6 changes: 3 additions & 3 deletions docs/mllib-evaluation-metrics.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -462,13 +462,13 @@ $$rel_D(r) = \begin{cases}1 & \text{if $r \in D$}, \\ 0 & \text{otherwise}.\end{
<td>Normalized Discounted Cumulative Gain</td>
<td>
$NDCG(k)=\frac{1}{M} \sum_{i=0}^{M-1} {\frac{1}{IDCG(D_i, k)}\sum_{j=0}^{n-1}
\frac{rel_{D_i}(R_i(j))}{\text{ln}(j+1)}} \\
\frac{rel_{D_i}(R_i(j))}{\text{ln}(j+2)}} \\
\text{Where} \\
\hspace{5 mm} n = \text{min}\left(\text{max}\left(|R_i|,|D_i|\right),k\right) \\
\hspace{5 mm} IDCG(D, k) = \sum_{j=0}^{\text{min}(\left|D\right|, k) - 1} \frac{1}{\text{ln}(j+1)}$
\hspace{5 mm} IDCG(D, k) = \sum_{j=0}^{\text{min}(\left|D\right|, k)} \frac{1}{\text{ln}(j+2)}$
Copy link
Member

@srowen srowen Aug 20, 2018

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

@yueguoguo I think the "- 1" in the upper bounds of the sum needs to be restored here?

</td>
<td>
<a href="https://en.wikipedia.org/wiki/Information_retrieval#Discounted_cumulative_gain">NDCG at k</a> is a
<a href="https://en.wikipedia.org/wiki/Discounted_cumulative_gain#Normalized_DCG">NDCG at k</a> is a
measure of how many of the first k recommended documents are in the set of true relevant documents averaged
across all users. In contrast to precision at k, this metric takes into account the order of the recommendations
(documents are assumed to be in order of decreasing relevance).
Expand Down