diff --git a/main.tex b/main.tex
index 16b5ecb1b62ae230484426e8e4f1dbfb8dc115b1..55b654c31bcddc5c665ca7c3262e92c33ec02713 100644
--- a/main.tex
+++ b/main.tex
@@ -311,7 +311,7 @@ On the recommendation side, however, it is  \textbf{media} that is the more like
 
 
 
-There are some interesting observations in the category-to-category transitions. While the highest transition CTRs for the base categories of  Berlin and Politics are to Media, for economy, it is to opinion, for sport it is to sport. The highest transition CTR for Culture is to the local category Berlin, and for world it is to politics followed by to Berlin.  The media category is the one that receive clicks from more categories than any others. The local category Berlin is the one that clicks on recommendations from more categories. 
+There are some interesting observations in the category-to-category transitions. While the highest transition CTRs for the base categories of  \textbf{berlin} and \textbf{politics} are to \textbf{media}, for \textbf{business}, it is to \textbf{opinion}, for \textbf{sport} it is to \textbf{sport}. The highest transition CTR for \textbf{Culture} is to the local categry,  \textbf{berlin}, and for \textbf{world} it is to \textbf{politics} followed by to \textbf{berlin}.  \textbf{Media}  is the one that is more likely to trigger clicks  upon recommendation. The local category \textbf{berlin} is the one that is more likely to trigger clicks on diverse recommendation categories. 
 
 \begin{table*}
 \caption{Transition CTR scores from base categories to recommendation categories.  The row categories represent the categories of base items and the column categories represent the recommendation categories. \label{heatmap}}