HCDA/sigir2016repo Changeset - d3d2889a13a1 · Centrum Wiskunde & Informatica (CWI)

@@ -150,14 +150,14 @@

\maketitle

\begin{abstract}

In a setting where  recommendations are provided to users when they are viewing particular items, what are the  factors that contribute to clicks on recommendations?  We examine factors that t trigger clicks on  recommended items in relation to the items the user  viewing and to which the recommendations are provided. More specifically, we examine the items from which clicks happen and what type of items get clicked. Are some items more likely to cause the user to click  on recommendations, and are some other recommendations more likely to be clicked? In short, are clicks on recommendations a function of the base item, or are they a function of the recommended items?  We attempt to explain the factors that trigger clicks on recommendations from different angles.

In a setting where  recommendations are provided to users when they are viewing particular items, what are the  factors that contribute to clicks on recommendations?  We examine factors that  trigger clicks on  recommended items in relation to the items the user  viewing and to which the recommendations are provided. More specifically, we examine the items from which clicks happen and what type of items get clicked. Are some items more likely to cause the user to click  on recommendations, and are some other recommendations more likely to be clicked? In short, are clicks on recommendations a function of the base item, or are they a function of the recommended items?  We attempt to explain the factors that trigger clicks on recommendations from different angles.

\end{abstract}

\section{Introduction}

 In a  study that investigated the relationship between the number of times  items are viewed and the the number of times clicks happened from those items  in several online publishers  \cite{said2013month}, it was reported that traditional news portals providing news and opinions on politics and current events are more likely to generate clicks on recommendation than special interest portals such as sports,  gardening, and auto mechanic forums.  Another study \cite{esiyok2014users}, using the same datasets investigated the impressions and clicks on the category level of items of one of the traditional news portals - Tagesspiegel ( a popular national news portal in Germany).  The finding was that there is a relationship between what the user is reading on and what the user  reads next. They  reported that the category local and sports enjoyed the most loyal readers, that is that a user reading on local items will more likely keep reading items of the same category.  % recommendations that were made to the different websites raising a queation as to whether the clicks on recommendations were because of nature of the online publishers or the recommendation items.this study, we focus on one traditional news portal, tagespiegel and examine it to find out factors that trigger recommendations on clicks or lack thereof. %wether some categories are more likely to recieve clicks on recommendations. We also even go further and look at what type of items are more likely to trigger more clicks than others.

 In a  study that investigated the relationship between the number of times  items are viewed and the the number of times clicks happened from those items  in several online publishers  \cite{said2013month}, it was reported that traditional news portals providing news and opinions on politics and current events are more likely to generate clicks on recommendation than special interest portals such as sports,  gardening, and auto mechanic forums.  Another study \cite{esiyok2014users}, using a similar dataset, investigated the impressions and clicks at level of the category  of items of one of the traditional news portals - Tagesspiegel (a popular German national news portal).  The finding was that there is a relationship between what the user is currently reading and what they  read next. They  reported that the category local and sports enjoyed the most loyal readers, that is that a user reading on local items will more likely keep reading items of the same category.  % recommendations that were made to the different websites raising a queation as to whether the clicks on recommendations were because of nature of the online publishers or the recommendation items.this study, we focus on one traditional news portal, tagespiegel and examine it to find out factors that trigger recommendations on clicks or lack thereof. %wether some categories are more likely to recieve clicks on recommendations. We also even go further and look at what type of items are more likely to trigger more clicks than others.

 While both these study are very related and relevant, they both did not investigate the relationship between the base items, the recommended items and the resulting clicks or lack thereof. In a recommendation setting where recommendation items are provided to  users on the items that the user is currently  viewing (henceforth referred to as base items),   what are the factor's that trigger user to click on recommendations?  Are the clicks a function of the base items or of the recommended items? Do some base items and some recommended items cause users to click on recommendations more than others, and if they do what explains this difference?

 While both these studies are very related and relevant, they  did not investigate the relationship between the base items, the recommended items and the resulting clicks or lack thereof. In a recommendation setting where recommendation items are provided to  users on the items that the user is currently  viewing (henceforth referred to as base items),   what are the factors that trigger user to click on recommendations?  Are the clicks a function of the base items or of the recommended items? Do some base items and some recommended items cause users to click on recommendations more than others, and if they do what explains this difference?

In this study we examine the factors that might trigger clicks on recommendations  from several angles. One angle  is from  the categories of the base items the user is currently reading. More specifically, are some categories of items the user is currently on  more likely to  cause the user to click on recommendations? Similarly, we examine the categories of the recommended items and investigate whether some are more likely to trigger clicks on themselves upon recommendation. We also investigate how  the categories of the base items and the categories of the recommendation items are related in the way they trigger clicks. %Are some categories  more likely to trigger clicks on some categories? For example, is political category more likely to trigger clicks on political categories, or another category such as local category?

@@ -180,7 +180,7 @@ We also go down to the item level and look at the relationships of the base item

\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.  The dataset is aggregated from the logs of our recommender systems that were 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 impressions (a user viewing an item), update (appearance of news item, or change of  content of existing item) and clicks[user clicking on recommendation.

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 impressions (a user viewing an item), update (appearance of news item, or change of  content of existing item) and clicks[user clicking on recommendation.

The three  recommendation that we used are two instances of Recency, and 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 recommendations every time  recommendation request is made

@@ -220,8 +220,8 @@ Figure \ref{fig:view_click} shows the plot of views and clicks for the $\mathit{

\begin{figure} [t]

\centering

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

\label{fig:view_click}

\caption{Plots of views and clicks on Tagesspiegel and Ksta.}

\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}

@@ -277,8 +277,8 @@ wissen&13500&4&0.03\\

   \hline

  \end{tabular}

  \label{tab:base}

\caption{Base Category }

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

\hfill

\parbox{.45\linewidth}{

@@ -301,8 +301,8 @@ wirtschaft&32955&15&0.05\\

   \hline

  \end{tabular}

  \label{tab:reco}

\caption{Recommendation Category}

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

\end{table*}

@@ -314,7 +314,7 @@ On the recommendation categories, however, it is the media category that trigger

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.

\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.}

\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

@@ -335,7 +335,7 @@ wissen&0.02&0&0&0&0.11&0.15&0&0&0\\

 \hline

  \end{tabular}

  \label{heatmap}

\end{table*}

@@ -373,8 +373,8 @@ To better visualize the results, we present  two plots. In Figure \ref{fig:view_

\centering

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

\label{fig:view_click_base}

\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.}

\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}

@@ -383,8 +383,8 @@ To better visualize the results, we present  two plots. In Figure \ref{fig:view_

\centering

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

\label{fig:view_click_reco}

\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.}

\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}