diff --git a/docs/mllib-evaluation-metrics.md b/docs/mllib-evaluation-metrics.md
index d9dbbab4840a..c65ecdcb67ee 100644
--- a/docs/mllib-evaluation-metrics.md
+++ b/docs/mllib-evaluation-metrics.md
@@ -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) - 1} \frac{1}{\text{ln}(j+2)}$
       </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).