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@@ -220,7 +220,7 @@ The predicted trend that a higher $tc$ results in a lower satisfaction and a hig
\hyporef{hyp:Evaluation:OnlyOneSatisfied} predicts that the satisfaction with the individual decision eventually reaches one and that no one is satisfied with the group recommender decision. This means the satisfaction change should reach minus one. \autoref{fig:Evaluation:tcCount} shows a downward trend that come close to one for heterogeneous and random groups. Also homogenous groups see a big drop but this drop does not reach one. Nonetheless, the steep drop suggests that the hypothesis holds in regards to reaching only one person satisfied with the individual decision when using quantiles that do not have to be integers. Also, satisfaction change in heterogeneous groups reaches close to minus one but this value is neither reached by random groups, nor by homogenous groups. The hypothesis therefore should not be seen as confirmed in that regard and further investigation is needed. \hyporef{hyp:Evaluation:OnlyOneSatisfied} predicts that the satisfaction with the individual decision eventually reaches one and that no one is satisfied with the group recommender decision. This means the satisfaction change should reach minus one. \autoref{fig:Evaluation:tcCount} shows a downward trend that come close to one for heterogeneous and random groups. Also homogenous groups see a big drop but this drop does not reach one. Nonetheless, the steep drop suggests that the hypothesis holds in regards to reaching only one person satisfied with the individual decision when using quantiles that do not have to be integers. Also, satisfaction change in heterogeneous groups reaches close to minus one but this value is neither reached by random groups, nor by homogenous groups. The hypothesis therefore should not be seen as confirmed in that regard and further investigation is needed.
During a group decision it is better to make one less person dissatisfied opposed to one more person satisfied. Therefore, this thesis uses $tc$ values that are closer to minima of unhappiness reduction than to the maxima of satisfaction change. The minima for heterogeneous is at $tc = 70$ therefore this is the chosen value for the evaluation of other aspects. For random groups the minima of dissatisfaction change can be found at $tc = 85$ which is the value used for all following analysis of random groups. For homogenous group dissatisfaction change is sinking until the highest value of $tc$. Because of that $tc = 94$ is used for analysis. During a group decision it is better to make one less person dissatisfied opposed to one more person satisfied. Therefore, this thesis uses $tc$ values that are closer to minima of unhappiness reduction than to the maxima of satisfaction change. The minima for heterogeneous is at $tc = 70\%$ therefore this is the chosen value for the evaluation of other aspects. For random groups the minima of dissatisfaction change can be found at $tc = 85\%$ which is the value used for all following analysis of random groups. For homogenous group dissatisfaction change is sinking until the highest value of $tc$. Because of that $tc = 94\%$ is used for analysis.
All scoring functions are similarly good in decreasing dissatisfaction. However the results differ when looking at satisfaction, Here least misery performs abysmal compared to the other scoring functions. In \autoref{fig:Evaluation:HeterogenousGroupIncrease} it results even in a hapiness reduction whereby multiplication and best average increase it. Overall multiplication seems to perform the best in most scenarios. This confirms findings in expirments with real people as described by \citeauthor{Masthoff2015} \cite[p. 755f]{Masthoff2015}. All scoring functions are similarly good in decreasing dissatisfaction. However the results differ when looking at satisfaction, Here least misery performs abysmal compared to the other scoring functions. In \autoref{fig:Evaluation:HeterogenousGroupIncrease} it results even in a hapiness reduction whereby multiplication and best average increase it. Overall multiplication seems to perform the best in most scenarios. This confirms findings in expirments with real people as described by \citeauthor{Masthoff2015} \cite[p. 755f]{Masthoff2015}.