diff --git a/Milestone-1-QA-template.tex b/Milestone-1-QA-template.tex
index d94f13bddf65bb36b8bcc1c2ac94aaa387d2c021..fa3d99db295354743cc684885336a749e89a9945 100644
--- a/Milestone-1-QA-template.tex
+++ b/Milestone-1-QA-template.tex
@@ -128,7 +128,7 @@ Implement $p_{u,i}$ using Spark RDDs. Your distributed implementation should giv
 \begin{itemize}    
         \item [\textbf{N.1}] \textit{Implement the k-NN predictor. Do not include self-similarity in the k-nearest neighbours. Using $k=10$,  \texttt{data/ml-100k/u2.base} for training output the similarities between: (1) user $1$ and itself; (2) user $1$ and user $864$; (3) user $1$ and user $886$. Still using $k=10$, output the prediction for user 1 and item 1 ($p_{1,1}$), and make sure that you obtain an MAE of $0.8287 \pm 0.0001$ on \texttt{data/ml-100k/u2.test}.} 
     
-    \item [\textbf{N.2}] \textit{Report the MAE on \texttt{data/ml-100k/u2.test} for $k = {10, 30, 50, 100, 200, 300, 400, 800, 942}$. What is the lowest $k$ such that the MAE is lower than for the baseline (non-personalized) method?} 
+    \item [\textbf{N.2}] \textit{Report the MAE on \texttt{data/ml-100k/u2.test} for $k = {10, 30, 50, 100, 200, 300, 400, 800, 943}$. What is the lowest $k$ such that the MAE is lower than for the baseline (non-personalized) method?} 
     
      \item [\textbf{N.3}] \label{q-total-time} \textit{Measure the time required for computing predictions (without using Spark) on \texttt{data/ml-100k/u2.test}. Include the time to train the predictor on \newline \texttt{data/ml-100k/u2.base} including computing the similarities $s_{u,v}$ and using $k=300$. Try reducing the computation time with alternative implementation techniques (making sure you keep obtaining the same results). Mention in your report which alternatives you tried,  which ones were fastest, and by how much. The teams with the correct answer and shortest times on a secret test set will obtain more points on this question.}
 \end{itemize}
diff --git a/Milestone-1.pdf b/Milestone-1.pdf
index b14d2ab6acdc30b2c165869bcb0a3762578152e3..0d74ac6cba215d70cc3eb62fd8814da6cb6cc876 100644
Binary files a/Milestone-1.pdf and b/Milestone-1.pdf differ
diff --git a/config.sh b/config.sh
index 70bf310774965d1d2628a1d7d03f2b4f1d96417a..38a9cd241d464e6b73f98fb38553d5b710d3b772 100755
--- a/config.sh
+++ b/config.sh
@@ -3,8 +3,8 @@ then
     export ML100Ku2base=hdfs://iccluster028.iccluster.epfl.ch:8020/cs449/data/ml-100k/u2.base;
     export ML100Ku2test=hdfs://iccluster028.iccluster.epfl.ch:8020/cs449/data/ml-100k/u2.test;
     export ML100Kudata=hdfs://iccluster028.iccluster.epfl.ch:8020/cs449/data/ml-100k/u.data;
-    export ML25Mr2train=hdfs://iccluster028.iccluster.epfl.ch:8020/cs449/data/ml-25m/r2.train;
-    export ML25Mr2test=hdfs://iccluster028.iccluster.epfl.ch:8020/cs449/data/ml-25m/r2.test;
+    export ML25Mr2train=hdfs://iccluster028.iccluster.epfl.ch:8020/cs449/data/ml-25m/r2-min-1.train;
+    export ML25Mr2test=hdfs://iccluster028.iccluster.epfl.ch:8020/cs449/data/ml-25m/r2-min-1.test;
     export SPARKMASTER='yarn'
 else 
     export ML100Ku2base=data/ml-100k/u2.base;