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 % Question for myself: Is it maybe possible to compute the category CTR's? Like a hitmap of the CTRs where the recommendations are subsidvided to their categories and a CTR is computed? I think so. We can also go durther and look at the contenet similarities. Further, we can look at what type of items trigger more clicks by selecting some items which generated more clicks and analyzing them. 
 
-At the item level, we tried to investigate whether there is a relationship, in triggering clicks on recommendations, between the base items and the recommended items. More specifically, are the base items that are more likely to trigger recommendation also the ones that are more likely to trigger recommendation upon recommendations? To accomplish this task, we first computed the CTRs separately  for base items and recommendation items and then intersected them to find the items that are in both. It is important to state here that we have more items in our recommendations than in our base items. This is  because we are only requested to provide recommendation to some items, while we have all items to use for our recommendation. We had a total of $\mathit{55708}$ items in our recommendation items and $\mathit{18967}$ on our base items. The intersection resulted in $\mathit{15221}$ items for which we looked at the CTRs they score when they are used as base items and as recommendation item. 
+At the item level, we  investigated whether the %re is a relationship, in triggering clicks on recommendations, between the base items and the recommended items. More specifically, are 
+the base items that are more likely to trigger recommendation are also the ones that are more likely to be clicked  upon recommendations. To accomplish this, we first computed the CTRs, separately,  for base items and recommendation items and then intersected them to find the items that are in both. It is important to state here that we have more items in our recommendations than in our base items. This is  because we are only requested to provide recommendation to some items, while we have all items to use for  recommendation. We had a total of $\mathit{55708}$ items in our recommendation items and $\mathit{18967}$ on our base items. The intersection resulted in $\mathit{15221}$ items for which we looked at the CTRs they score when they are used as base items and as recommendation items. 
 
-To better visualize the results, we present  two plots. In Figure \ref{fig:view_click_base}, we present  plots generated by sorting the results by base CTR scores. Blue plot is base CTR and red plot is recommendation CTR.  What we observe here is that although the base items that are more likely to trigger clicks on recommendations are also the items that are more likely to trigger clicks upon their recommendations, there are many other items that are more likely to trigger clicks upon their recommendation, but not when they are base items. To visualize this better, we also sorted the results by recommendation CTRs, and we obtained the plots in Figure \ref{fig:view_click_reco}. We observe here the base items (the blue lines) that are more likely to trigger clicks on recommendation are a subset of  the recommendation items that are more likely to trigger clicks upon their recommendation. The discrepancy we observed might have to do with the fact that we had a limited access to the base items while we have a full access to the items for recommendation. 
+To better visualize the results, we present  two plots. In Figure \ref{fig:view_click_base}, we present  plots generated by sorting the results by base CTR scores. The blue plot is for base CTR and red plot is for recommendation CTR.  What we observe here is that although the base items that are more likely to trigger clicks on recommendations are also the items that are more likely to trigger clicks upon their recommendations, there are many other items that are more likely to trigger clicks upon their recommendation, but they do not do so as base items. To visualize this better, we also sorted the results by recommendation CTRs, and we obtained the plots in Figure \ref{fig:view_click_reco}. We observe here the base items (the blue line) that are more likely to trigger clicks on recommendation are a subset of  the recommendation items that are more likely to trigger clicks upon their recommendation. %The discrepancy we observe might have to do with the fact that we had a limited access to  base items while we have a full access to the items for recommendation. 
 
 
  \begin{figure} [t]
