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30_Thesis/sections/20_related_work.tex
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\chapter{Related Work}
\label{ch:Related_Work}
\todo[inline]{proofread and improve chapter, possibly go more into detail}
This chapter discusses related work. The approaches presented are either group recommenders, group-based configuration systems or recommender systems for configuration. However no literature combines these approaches to get a group recommendation for group-based configuration.
\section{Group Recommender Systems}

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30_Thesis/sections/50_design_and_implementation.tex
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\end{description}
The API is implemented with a minimal amount of functions and the recommender currently only supports one product at a time. However, the architecture is built in a way that it can be easily extended into supporting multiple products.
The general architecture adheres to the model-view-controller \todo[]{wir das Konzept im Litertaurteil eingeführt? Oder ist das ein allbekanntes Konzept in der Informatik? (mir wars neu)} inspired architecture.
The general architecture adheres to the model-view-controller inspired architecture.
Data is stored and retrieved using data access objects. Therefore, the currently used simple TinyDB database backend can be switched to a different one easily.
\section{Database}
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\section{Scoring Functions}
\label{sec:DesignImplementation:ScroingFunctions}
Scoring functions are implemented in a modular fashion. There are different types of scoring functions. Some are meant for evaluating user preferences (and inherit from the class \emph{PreferenceScoringFunction}), others are meant for penalising changes in the current configuration state and therefore inherit from \emph{ConfigurationPenalty}. The last type of scoring function is a composite scoring function that consists of one or multiple scoring functions and combines their scores to one. It uses the \emph{composite} design pattern. \todo[]{wurde das im Literaturteil erklärt?} These functions allow ´the flexible combination of functions and, in addition due to their underlying structure they can use similar components. For example, preference scoring functions use a pipeline to first convert preferences to a list of values. This is done using \emph{PreferenceToListFunction}. A list can be modified with a \emph{ListToListFunction}. This step is optional. The last mandatory step is to convert the list to a value using \emph{ListToValueFunction}. After this step, the value can be modified further, for example with a threshold function or any other function. All these components allow the easy and quick creation of new scoring functions. A scoring function factory is used to assemble a scoring function based on input parameters. This provides flexibility for quickly implementing new types of scoring functions.
Scoring functions are implemented in a modular fashion. There are different types of scoring functions. Some are meant for evaluating user preferences (and inherit from the class \emph{PreferenceScoringFunction}), others are meant for penalising changes in the current configuration state and therefore inherit from \emph{ConfigurationPenalty}. The last type of scoring function is a composite scoring function that consists of one or multiple scoring functions and combines their scores to one. It uses the \emph{composite} design pattern. These functions allow ´the flexible combination of functions and, in addition due to their underlying structure they can use similar components. For example, preference scoring functions use a pipeline to first convert preferences to a list of values. This is done using \emph{PreferenceToListFunction}. A list can be modified with a \emph{ListToListFunction}. This step is optional. The last mandatory step is to convert the list to a value using \emph{ListToValueFunction}. After this step, the value can be modified further, for example with a threshold function or any other function. All these components allow the easy and quick creation of new scoring functions. A scoring function factory is used to assemble a scoring function based on input parameters. This provides flexibility for quickly implementing new types of scoring functions.
\section{Technologies}

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30_Thesis/sections/60_evaluation.tex
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A metric is required to carry out the validation. The proposed metric is the metric of satisfaction. This metric was created because pertinent literature does not provide metrics usable for this thesis. Satisfaction is quantified in this thesis by a threshold metric. A user's preference is used to calculate a rating for each possible solution. Each configuration solution gets an individual score determined by the user's preferences. The score is calculated using the average of a user's preference for each characteristic that is part of the configuration. The result allows a configuration to be compared to all other configurations and ranked according to the percentage of configurations that it beats for a specific user. The threshold metric consists of two parameters. First the threshold center $tc$ and second the satisfaction distance $sd$. The threshold for a satisfied person is at $tc + sd$ and for a dissatisfied person is at $tc - sd$. If a recommendation lies in between these two thresholds the person is classified to neither be satisfied nor be dissatisfied with the solution. For this thesis $sd=5\%$ will be used. This choice is based on the assumption that people switch from satisfied to dissatisfied rather quickly \todo{find a source psychology}. Therefore, the parameter considered in this thesis is $tc$. An example is the choice of $tc = 60\%$. This results in a person satisfied with a recommendation if it is better than at least $65\%$ of all possible finished configurations. In contrast, a person is dissatisfied if the recommendation is not better than $55\%$ of all possible finished configurations. A recommendation that is better than at least $55\%$ and not better than $65\%$ of all possible solutions is considered neutral by the individual.
\todo{(optional) visualize tc value with an example configuration}
Different $tc$ values allow to model different situations. A situation with a low willingness to compromise is modelled by a high $tc$. A contrary situation with a group that has a high willingness to compromise is modelled by a low $tc$.
A satisfaction and dissatisfaction classification allows groups to be measured by the amount of people that are satisfied and dissatisfied. Moreover, changes in satisfaction and dissatisfaction for different parameters can be compared. A reasonable $tc$ value has to be found for groups otherwise any derived metrics will not show any meaningful results.