From a073d0e868bdfbb64aeaf291e942e7ee06af8570 Mon Sep 17 00:00:00 2001 From: "hannes.kuchelmeister" Date: Tue, 31 Mar 2020 12:21:15 +0200 Subject: [PATCH] add explanations for hypotheses --- 30_Thesis/sections/60_evaluation.tex | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/30_Thesis/sections/60_evaluation.tex b/30_Thesis/sections/60_evaluation.tex index 18368c1..059ee8d 100644 --- a/30_Thesis/sections/60_evaluation.tex +++ b/30_Thesis/sections/60_evaluation.tex @@ -180,16 +180,26 @@ These user profiles can be used to generate rather homogenous groups but also to Understanding data is made easier by first posing hypotheses. This section gives an overview over the hypothesis used during data analysis. \begin{enumerate}[font={\bfseries},label={H\arabic*}] - \item \label{hyp:Evaluation:MaximumMinimum} Highest improvements with group recommendation are when the amount of people satisfied with the dictators decision is slightly lower than two. Respectively that holds true for dissatisfaction. + \item \label{hyp:Evaluation:MaximumMinimum} Highest improvements with group recommendation are when the amount of people satisfied with the dictator's decision is slightly lower than two. Respectively that holds true for dissatisfaction. \item \label{hyp:Evaluation:HigherTcLessSatisfied} A higher $tc$ value results in less satisfied people and more unsatisfied people with regard to the dictator's decision. \item \label{hyp:Evaluation:OnlyOneSatisfied} There exists a $tc$ value which causes only one person to be satisfied with the dictator's decision and no one is satisfied with the group recommender's decision. \item \label{hyp:Evaluation:HomogenousMoreSatisfied} Homogeneous groups have more satisfied members with the recommender's decision but also with the dictator's decision compared to heterogeneous groups. \item \label{hyp:Evaluation:HeterogenousBiggerSatisfactionIncrease} More heterogeneous groups see a bigger satisfaction increase than less heterogeneous groups when comparing the dictator's decision with the recommender's decision. \item \label{hyp:Evaluation:StoreSizeBetterResults} A higher amount of stored finished configurations results in a higher amount of satisfied and a lower amount of dissatisfied group member. - \item \label{hyp:Evaluation:AggregationStrategies} Multiplication and best average aggregation strategies perform better than least misery. % These strategies are listed by \citeauthor{Masthoff2015} \cite[p. 755f]{Masthoff2015} and multiplication and best average came out as the best in most studies. Least misery was in some listed as performing worst. Therefore it fares worse than the other strategies here. + \item \label{hyp:Evaluation:AggregationStrategies} Multiplication and best average aggregation strategies perform better than least misery. % \end{enumerate} -\todo[inline]{explain hypotheses} +These hypotheses require some explanation about the reasoning behind them. + +\begin{description} + \item[\hyporef{hyp:Evaluation:MaximumMinimum}] This expectation is made because the assumption is made that in a real situation a group of four with a bit less than two people happy on average (with a dictator's decision) has enough room for improvement so that potentially three people can be lifted to satisfaction. Meaning that at least one more person is satisfied with the compromise. Potentially in some groups it might even be possible to then lift another person from dissatisfaction towards a neutral attitude. With a base satisfaction level being higher this is assumed to reduce the possibility to make an additional group member satisfied. + \item[\hyporef{hyp:Evaluation:HigherTcLessSatisfied}] A higher $tc$ value causes a person to be unsatisfied with a higher amount of configurations. Also it causes a person to be satisfied with less configurations. Therefore recommending a random configuration causes the chance of making an individual satisfied sink while increasing the chance of that person being unsatisfied. Already the change in probability leads to the assumption that this should be seen with non random recommendations too. + \item[\hyporef{hyp:Evaluation:OnlyOneSatisfied}] A $tc$ value that reaches a high enough level eventually should make only the dictator herself satisfied with the dictator's decision. The bar for satisfaction lies so high that any group recommendation will cause the dictator to also be not satisfied or at least neutral with the group decision. This can be understood as that in a group where nobody is willing to compromise everyone would be only happy with their own decision. Having to members with identical interest of course results in this effect not being present but this is expected to be rare for a group site of four. + \item[\hyporef{hyp:Evaluation:HomogenousMoreSatisfied}] As the interest in homogenous groups are more aligned there is an expectation that the overall hapiness levels for more homogenous groups is higher. If the base level is higher already it is likely that even just a slight increase lifts recommendations for homogenous groups to satisfaction levels not reachable by heterogeneous groups. + \item[\hyporef{hyp:Evaluation:HeterogenousBiggerSatisfactionIncrease}] The assumption is made that in more heterogeneous groups the satisfaction with the dictator's decision is less. Therefore there is a higher possible increase. A homogenous group that already satisfies all group members with the dictator's decision cannot see an increase in satisfaction therefore the assumption is made, that with a higher amount of people dissatisfied and not satisfied with the dictator's decision, there will be more people that can be lifted into happiness and therefore the increase will be bigger. However a group that has contradicting interest actually might not be able to reach high satisfaction levels. + \item[\hyporef{hyp:Evaluation:StoreSizeBetterResults}] This hypothesis is born by the fact that having a bigger pool of configurations to choose from increases the chances of having a good recommendation. This of course requires the assumption that aggregation strategies that pick recommendations pick configurations that also fare better in the chosen satisfaction metric. If it is not the case this hypothesis should not hold. + \item[\hyporef{hyp:Evaluation:AggregationStrategies}] Best average and multiplication are strategies that are listed listed by \citeauthor{Masthoff2015} \cite[p. 755f]{Masthoff2015} as performing best in some of the listed online experiments. Therefore it is reasonable to assume that they perform well. Least misery was listed in some as performing worst. Therefore there is an expectation of it faring less good than other group aggregation strategies. +\end{description} \section{Findings} \label{sec:Evaluation:Findings}