start writing choosing tc section

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@@ -204,19 +204,22 @@ Understanding data is made easier by first posing hypothesises. This section giv
\section{Findings}
\label{sec:Evaluation:Findings}
\subsection{Choosing smd}
\subsection{Choosing tc}
The data confirms \hyporef{hyp:Evaluation:LowSMD} and \hyporef{hyp:Evaluation:HighSMD}. \autoref{fig:Evaluation:HappyUnhappySMD} shows a clear trend where with a higher $smb$ the amount of satisfied and dissatisfied is reduced. However, with homogenous group the dissatisfaction is constantly at zero and the satisfaction only takes a slight dip. This suggests that with homogenous groups an individual deciding for the whole group is a viable option. Random groups are more uniform than heterogeneous groups. This can be seen by a lower amount of dissatisfied group members and a higher amount of satisfied ones.
\hyporef{hyp:Evaluation:MoreSatisfiedLessIncrease} states that the amount of hapiness increase should be limited if we choose a low $smb$ because there is less people that can be changed to satisfied. This effect however is not observable in the data even when $smb = 5$. Neither can the adverse effect with a high $smb$ and dissatisfaction change. Yet it might be possible that this effect can be observed when choosing $smb > 35\%$. The effects of \hyporef{hyp:Evaluation:OnePersonSatisfied} can also not be seen in the data. Moreover the effect of \hyporef{hyp:Evaluation:NoOnedissatisfied} could also not be seen in the data but it was noticeable that a high $smb$ reduces unhappiness reduction. Nonetheless these effects still might occur when testing a higher $smb$ than $35\%$.
The $smd$ will be fixed from now on to $15\%$. This allows to still show the improvements of the recommender for rather heterogeneous groups but also prevents a too high reduction in dissatisfied group members thereby preventing any effect when just looking at dissatisfaction. As \autoref{fig:Evaluation:HappyUnhappySMD} shows homogenous groups are already too happy and have no dissatisfaction. This is why they will not undergo any more evaluation.
To get an understanding of the data all parameters except the $tc$ will be fixed. The preference aggregation strategy looked at is multiplication. The configuration database is used with all possible solutions (which is 148 in total). This results in a bigger visible effect as the recommender has access to all possible configurations.
\begin{figure}
\centering
\includegraphics[width=1\textwidth]{./figures/60_evaluation/smd_chamge_happy_unhappy.pdf}
\caption{The average satisfaction and dissatisfaction over the individual decision depending on \textbf{group type} and $smd$.}
\label{fig:Evaluation:HappyUnhappySMD}
\includegraphics[width=1\textwidth]{./figures/60_evaluation/tc_change__multi__db-size-148.pdf}
\caption{The average satisfaction and dissatisfaction change based on $tc$ with a database size of 148 and multiplication as aggregation strategy.}
\label{fig:Evaluation:tcChange}
\end{figure}
\begin{figure}
\centering
\includegraphics[width=1\textwidth]{./figures/60_evaluation/tc_dictator__multi__db-size-148.pdf}
\caption{The average satisfaction and dissatisfaction with the dictator's decision based on $tc$ with a database size of 148 and multiplication as aggregation strategy.}
\label{fig:Evaluation:tcCount}
\end{figure}
\subsection{Analysing Data}