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\section{Dataset}

We used Plista\footnote{http://orp.plista.com/documentation}  dataset collected from user-item interaction with the tagesspiegel.com news portal, German online news and opinions portal,  over more than two months, from 15-04-2015 to 04-07-2015. Items in tagesspiegel are manually placed under $\mathit{10}$ categories, $\mathit{9}$ of which we investigate in this study.  The dataset is aggregated from the logs of the  recommender systems that we used during our participation in the CLEF NewsREEL 2015 challenge \cite{kille2015overview}. The challenge offered   participants the opportunity to plug their recommendation algorithms to Plista\footnote{http://orp.plista.com/documentation} and provide recommendations to  real users visiting online publishers. Plista is a framework that connects recommendation providers such as ourselves and recommendation service requester such as online news portals. Participation in the challenge enabled us to collect information of user-item interaction such as impression (a user viewing an item), update (appearance of new item, or change of  content of existing item) and click (a user clicking on recommendation item). 

The three  recommendation algorithms that we used are two instances of \textbf{Recency}, and \textbf{RecencyRandom}. The Recency algorithm keeps the most recently viewed or updated items and recommends the top most $mathit{k}$  recent items every time a recommendation request is made. The RecencyRandom recommender keeps the most recent $\mathit{100}$ items at any time and recommends, randomly, the requested number of items every time a  recommendation request is made. 

 Unfortunately, the click information did not include whether the click on recommendation was on our recommendations or on someone else's recommendations. Since we know the user and the base item for which we recommended and the recommended items, we considered a click notification on one of our recommended items as a click on our recommendation if that click happened with in $\mathit{5}$ minutes  from the time of our recommendation.  From the combined collected dataset,  we extracted the base item, the category of the base item, the recommended item and the category of the recommended item, the number of times a recommendation item has been recommended to a base item (view) and the number of times that the recommended item has been clicked from the base item. A sample dataset is presented in Table \ref{tab:sample}.

\begin{table}

\caption{A sample dataset. B is the base item id, R  is the recommendation item id, and B-Cat and R-cat are the categories of the base item and the recommendation item, respectively. \label{tab:sample}}

\centering

  \begin{tabular}{|l|l|l|l|l|l|l|}

\hline

           B  &  B-Cat & R& R-Cat &View&Click & CTR \\

           \hline

229397219 &229495114   &   Berlin &     Berlin &  17   &  1  & 5.88\\

230306628 &230291175&     politics &     wissen &   14 &    1 & 7.14\\

40485126 & 225589114  &      Berlin &    politics  & 2    & 0  & 0.00\\

   \hline

  \end{tabular}

\end{table}

%Plista is a company that provides a recommendation platform where recommendation providers are linked with online publishers in need of recommendation sertvice.

% It is not easy to get the exact number of times a recommendation item is recommended to a   certain base item since the logs did not include wWe assume that the number of times a base item has been viewed as the number of times recommendations were shown. We assume this to be a fair assumption as recommendation were sought each time a an item was viewed by a user. % Although each time an item is viewed, more than one item (usually 5 items) are shown to the user as recommendations, we just count the number of clicks that have happened from those items 

% regardless of which items are clicked. 

\section{Results and Analysis}

Our dataset consists of a total of $\mathit{288979}$ base-item recommendation-item pairs. To see the relationship between views and clicks, we first  sorted the dataset according to views and then  normalized the \textbf{view} and \textbf{click} counts by the total number of views and total number of click, respectively. We then slected the top $\mathit{1000}$ pairs and plotted the views  and the clicks. The reason for normalization is to be able to plot them together for easy comparision. %The selection of only $\mathit{1000}$ pairs is because the more items we use, the more difficult is to see . 

Figure \ref{fig:view_click} shows the plot of views and clicks for the $\mathit{1000}$ pairs. The blue plot  is for views and is smooth since the data was sorted by views. The red plot is for the corresponding clicks on recommendations. We observe that the clicks do not follow the views, an indication  that t clicks do not correspond with the number  of times that a recommendation items is recommended to a base item. This is the reason we set out to investigate, to begin with.  The ragged click plot shows that some items are more likely to trigger clicks on recommendations than others.  What can possibly explain this observation?  What causes these difference between the number of views and the number of clicks? 

\begin{figure} [t]

\centering

\includegraphics[scale=0.5]{img/tage_view1_click000.pdf}

\caption{Plots of views and clicks on Tagesspiegel. The Plots are normalzized by the total views and total clicks. The Blue plot (bottom) ais the sorted view plot and the red plot is the corresponding click plot.\label{fig:view_click}}

\end{figure}

% 

%  \begin{figure} [t]

% \centering

% \includegraphics[scale=0.5]{img/tage_view100.pdf}

% 

% \label{fig:view100}

% \caption{Plot of the most viewed 100 items}

% \end{figure}

%  \begin{figure} [t]

% \centering

% \includegraphics[scale=0.5]{img/tage_click100.pdf}

% 

% \label{fig:click100}

% \caption{Plot of the clicks triggered from the 100 most viewed items}

% \end{figure}

% 

% 

\subsection{Categories of Base and Recommendation Items}

To start to explain the difference between the view plot and the click plot observed in \ref{fig:view_click},  we aggregated  views and clicks by the $\mathit{9}$ categories of   items  that the items are placed under in the Tagesspiegel website. The aggregation gives us two results: view and click counts the  categories in base and in recommendation.   With the categories, we attemopt to answer two questions: 1)  is there a relationship between the category of the base item and the likelihood of triggering  a click on recommendation, and  2) is there a relationship between the category of the recommended item and the likelihood of triggering a click upon its recommendation?    Tables \ref{tab:base} and \ref{tab:reco} present  the views, clicks and CTR scores. The results are sorted by CTR scores. 

We observe that there is a difference between the base categories and the recommendation categories with respect to the likelihood of triggering clicks.  In the base categories, the  \textbf{politics} is more likely to triggers clicks  than any other category \textbf{opinion} and \textbf{world}. Special categories such as \textbf{culture} and and \textbf{knowledge} are the least likely to trigger clicks on recommendations. This is consistent with previous findings that reported special interest portals generated  less clicks on recommendations than traditional and mainstream news and opinion portals. 

\begin{table*}

\caption{A table showing the views, clicks, and ctr of the 12 categories of Tagesspiegel on the basis of the base items. This table shows the views, clicks and CTRs of the base item. A click for base item happens when an item recommended to it is clicked. }

\parbox{.45\linewidth}{

\centering

\begin{tabular}{|l|l|l|l|l|}

\hline

           category  &  Views & Clicks & CTR (\%)\\

           \hline

politics&73197&178&0.24\\

media&22426&50&0.22\\

weltspiegel&37413&77&0.21\\

wirtschaft&30045&61&0.2\\

sport&29812&58&0.19\\

berlin&123595&129&0.1\\

meinung&4611&3&0.07\\

kultur&21840&11&0.05\\

wissen&13500&4&0.03\\

   \hline

  \end{tabular}

\caption{Base Category \label{tab:base}}

}

\hfill

\parbox{.45\linewidth}{

\centering

 \begin{tabular}{|l|l|l|l|}

\hline

           category  &  Views & Clicks & CTR (\%)\\

           \hline

medien&22147&68&0.31\\

politik&68230&170&0.25\\\

berlin&123559&188&0.15\\

weltspiegel&37535&58&0.15\\

sport&28160&36&0.13\\

meinung&4925&5&0.1\\

kultur&23278&21&0.09\\

wissen&15650&10&0.06\\

wirtschaft&32955&15&0.05\\

   \hline

  \end{tabular}

\caption{Recommendation Category \label{tab:reco}}

}

\end{table*}

On the recommendation side, however, it is  \textbf{media} that is the more likely to triggers  clicks upon recommendation, followed by  \textbf{politics} and the local category (\textbf{Berlin}. The two least performing categories are \textbf{business} and \textbf{knowledge},  similar to the least performing  categories in base. So, overall, it seems that the likelihood of triggering clicks by the categories shows a difference when they are in base and recommendation.  In general, the  categories have higher CTRs in recommendation that in base. To gain further insight, we looked at the CTRs of transitions from base category to recommendation category. The aim of this is to find out whether some base categories are more likely to trigger clicks on some recommendation categories. The results are presented in Table \ref{heatmap}.

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}}

  \begin{tabular}{|l|l|l|l|l|l|l|l|l|l|l|l|}

\hline

&Berlin&politics&wirtschaft&sport&kultur&weltspiegel&meinung&medien&wissen\\

\hline

berlin&0.14&0.08&0.06&0.05&0.06&0.12&0.12&0.16&0.06\\

politik&0.2&0.39&0.06&0.12&0.04&0.3&0&0.73&0.1\\

wirtschaft&0.15&0.4&0.07&0.13&0.36&0.13&0.46&0.21&0\\

sport&0.14&0.27&0&0.68&0.05&0.18&0&0.27&0.07\\

kultur&0.11&0&0&0.06&0.07&0&0&0.07&0\\

weltspiegel&0.24&0.27&0.06&0.13&0.17&0.13&0&0.4&0.18\\

meinung&0.06&0&0&0&0&0&1.85&0.32&0\\

medien&0.1&0.85&0&0.06&0&0.08&0&0.16&0\\

wissen&0.02&0&0&0&0.11&0.15&0&0&0\\

 \hline

  \end{tabular}

\end{table*}

%  For example  if we look at the category of politics , we see that the CTR from politics to politics is the highest than from politics to any other category.  We also observe that the CTR from local category Berlin to politics is higher than from the local category Berlin to any other category including to itself. A little surprising result is the high CTR from media to politics. 

% The way we extracted our recommendations and clciks is a little uncertan. In the Plista setting, when click results are reported to users, they are not known whose recommendations are being clicked. So while we know our recommendation, we do not know for sure how much of the click notifications that we recieve belong to our recommendations. To extract our clciks, we introduced a time frame of 5 minutes. That is if the click notification happens in with in a range of time, in our case 5 minutes, we consider the clcik is on our recommendations. We consider the click information is a bit inflated for users might not stay for more than 5 minutes. While the actual CTR might be a bit inflated as a result of the inflated number of clicks, we consider the relative scores as indicative of the true difference.

% To find out therelationship between base item recommendation pairs that resulted in high CTR scoores, we selected some item-recommendations pairs. To avoid selecting item-recommendation pairs that have very low views and clicks which is usually the type of combination that results in high CTR scores, we first sort our data according to views, and according to clicks. Using  cutt off values, we repeat the intersection until we find the items that have both the highest view and the hight clicks. Using this approach we selected 12 item-recommendation pairs and out of them we selected the 5 pairs that have the highest score. These pairs are presented in Table \ref{}

\subsection{Item-level Base and Recommendation CTRs}

% We look at the two types of item-level CTR's:the base item CTRs and the recommendation CTRs.  The base item CTR measures how likely the base item is to trigger clicks on recommendation. We assume that part if clicking on recommendations is a function of the item the user is reading. this is corroborated by the category-level CTr's that we looked at above in thesense that some categories do not generate clicks. even if the item are from clickable categories. The recommendation CTR's ameasures how likely the item is to recieve a click when recomened to a user regardless of the category of the base item.  But, should we not be concerned about the base item? 

% We plan to extract a sample of base items with  recommended and clicked items and separate them into clicked and rejected recommendations. We then compare the contenet of the clicked items with the contenet of the base item. We also do the same with the rejected items and see if there is any similarities/differences bertween these two categories.  The sepration of clicked and rejected items and comparing them to the base item is similar to the sepration of recommended moviews into viwed and ignored in \cite{nguyen2014exploring}. 

% 

% On the same dataset, there has been a study on the transition probababilities of users on the categories  This study was on genral reading. In this study 1) we repeat the same study on a dataset from a different time and 2) we analyze results in terms of similarity of content with the base items. 

% 

% 

% 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. 

 \begin{figure} [t]

\centering

\includegraphics[scale=0.45]{img/base_reco_ctr_sorted_by_base.pdf}

\caption{Plots of CTRs on base items and recommended items. Plots are generated by first sorting results according to base CTRs. Blue plot is base CTR and red plot is recommendation CTR. \label{fig:view_click_base}}

\end{figure}

 \begin{figure} [t]

\centering

\includegraphics[scale=0.45]{img/base_reco_ctr_sorted_by_reco.pdf}

\caption{Plots of CTRs on base items and recommended items. Plots are generated by first sorting results according to recommendation CTRs. Blue plot is base CTR and red plot is recommendation CTR. \label{fig:view_click_reco}}

\end{figure}

\section{Discussion and Conclusion}

In this study, we attempted to explain the factors that cause  clicks on recommendations. We specifically investigated whether  clicks on recommendations are a function of the base items, or a function of the recommended items. We attempted to explain that by looking at the categories of items and the transitions between the categories. We found that indeed the category of the items explains some of the discrepancy between the likelihoods of triggering clicks both as base items and recommendation items in the sense that some base categories and some recommendation categories are more likely to trigger clicks than others.

There is, however,  a difference between the categories in their likelihood to trigger clicks as  base category and in recommendation category. As base category, the politics category is the most likely to trigger clicks on recommendations followed by media. In recommendations, however, it is the media followed by politics that trigger clicks upon their recommendation. The results suggest that  click on recommendations is a function of both the base items and the recommended items. This is indicated by the fact that some categories are less or more  likely to generate clicks on recommendation whether as base, which means we should not recommend to those items, nor as recommendations, that means we should not recommend them. The results also show that the performance of the categories as base and recommendations are not similar.

The investigation of the transition CRTs shows which categories are more likely to trigger clicks on which categories. This result suggests that recommendation can be improved by recommending some categories to those categories where they are more likely to get clicked. For example, we observe that it is more likely to receive clicks if we recommend media items to the  politics category and to the local category (Berlin). Similarly, we observe that recommending sports items to sports items is much more likely to trigger clicks. These results suggest that there is a way to improve recommender system by leveraging category information of items. 

We hope that this work can contribute to the understanding of factors affecting recommender systems. We have shown that category-level information can take us a long way in explaining why clicks on recommendations happen. Item level information also showed that there is a relationship between base items that are more likely to trigger clicks and those recommendation items that are more likely to trigger clicks. This all suggests that leveraging information at both the category and item levels is important to improve recommender systems. As a future work, we would like to investigate the factors that lead to clicks on recommendation using a larger  dataset and at the content level of the  items. 

% An idea, maybe show the variance of the categories in terms of their CTR? Another thing we can do is to explore the high achieving base itemsand the high achiving recommended itesm and see if they are some how the same items. We also do similar thing with lowe achving base item and recommended items. Is this holds, then clearly it indicates that a big factor is not about the current context, but just the nature of the items themselves, both ion the base items, and in the recommended items.  This is going to gold , as it already shows in the groups. But, we can also zoom in on the politics items and see if that holds too. Another thing we can consider is find base items and recommended items with big variance and study them with the view to finding the causes in terms of categories and also in terms of contenet. The variance of a recommendation item tells us information that is it is recommended to some values it makes sense, but if to others, it does not. This can also be studied at a particlat group's 

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