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30_Thesis/sections/60_evaluation.tex
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@@ -233,7 +233,7 @@ This section gives an overview over the hypothesis tested during data analysis.
\begin{itshape}
\label{hyp:Evaluation:AggregationStrategies} Multiplication and best average aggregation strategies perform better than least misery across the board.
\end{itshape} \medskip \\
Best average and multiplication are strategies that are performing best in some of the, by \citeauthor{Masthoff2015} \cite[p. 755f]{Masthoff2015}, listed online experiments. Therefore it is reasonable to assume that they perform well here, too. Least misery was listed in some studies as performing worst. Therefore there is an expectation of it faring less good than other group aggregation strategies.
Best average and multiplication are strategies that are performing best in some of the, by \citeauthor{Masthoff2015} \cite[~ 755f]{Masthoff2015}, listed online experiments. Therefore it is reasonable to assume that they perform well here, too. Least misery was listed in some studies as performing worst. Therefore there is an expectation of it faring less good than other group aggregation strategies.
\end{hypothesis}
\section{Results}
@@ -311,7 +311,7 @@ After description of the data the remaining hypotheses are discussed.
The data shows that having a larger configuration database causes the amount of satisfied group members to be greater than recommendation's using a smaller database. With dissatisfaction the same is seen in inverse. A larger configuration database causes the number of dissatisfied group members to drop compared to a small database. However, in some runs there have been instances of least misery that have seen a slight drop. This can be seen in \autoref{fig:Evaluation:HeteroSatisfaction} when comparing $74$ and $148$ as number of stored configurations. Why this happens is not entirely clear but a cause of that might be that least misery just takes into account the worst performing group member of the group. Therefore it is possible that there is a second slightly worse solution, when comparing least misery scores, which actually has a slight advantage in terms of dissatisfaction. Having this second best configuration can cause it to be in the second database partition therefore resulting in less dissatisfaction on average. \autoref{hyp:Evaluation:StoreSizeBetterResults} therefore is supported by the data but it does not fully hold up when looking at least misery.
\autoref{hyp:Evaluation:AggregationStrategies} states least misery performs worse than multiplication. For a change in satisfaction this can be seen across the board however for dissatisfaction change this is not true everywhere. \autoref{fig:Evaluation:HeteroSatisfaction} shows that least misery performs better than best average in terms of dissatisfaction reduction. This behaviour possibly occurs because an average metric yields the same results for heavily polarised decisions and decisions that everyone feels neutral about. Least misery on the other hand takes only the group member least satisfied with the decision into account therefore this metric performs better. However in other cases it performs visibly worse. Also of note is multiplication performs best across the board. This supports the findings by \citeauthor{Masthoff2015} \cite[p. 755f]{Masthoff2015} and also shows that the satisfaction model does show some similar results to online evaluations.
\autoref{hyp:Evaluation:AggregationStrategies} states least misery performs worse than multiplication. For a change in satisfaction this can be seen across the board however for dissatisfaction change this is not true everywhere. \autoref{fig:Evaluation:HeteroSatisfaction} shows that least misery performs better than best average in terms of dissatisfaction reduction. This behaviour possibly occurs because an average metric yields the same results for heavily polarised decisions and decisions that everyone feels neutral about. Least misery on the other hand takes only the group member least satisfied with the decision into account therefore this metric performs better. However in other cases it performs visibly worse. Also of note is multiplication performs best across the board. This supports the findings by \citeauthor{Masthoff2015} \cite[~p. 755f]{Masthoff2015} and also shows that the satisfaction model does show some similar results to online evaluations.
To go back to in \autoref{sec:Evaluation:Questions} posed evaluation questions this section has shown that for random and heterogeneous groups the recommender performs better than a dictator. The average satisfaction depends on the chosen parameters but for the chosen value range average satisfaction with the recommender decision lies above two and can reach close to three satisfied group members for a high number of stored configurations and for some group types. The amount of stored finished configurations plays an important role in performance but with a fraction of stored configurations the recommender still yields good results. This shows that the recommender provides useful decision support for helping in group decisions. It provides a solid basis for groups and can help their group decision. Most decisions the recommender makes improve group satisfaction which shows that the recommender is able to be used to improve group decisions.