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2020-05-08 14:47:47 +02:00
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30_Thesis/sections/40_concept.tex
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@@ -221,7 +221,7 @@ This thesis will use multiple scoring functions. Among those are ones for least
\subsection{Preference Scoring}
\label{subsec:Concept:ReccomendationGeneration:PreferenceScoring}
All of the aggregation functions mentioned in \autoref{subsec:Concept:SolutionGeneration:ScoringFunction} have one preference per product. For configuration where a preference for all characterises exists there needs to be a function that combines the preferences of one user into her configuration score. After one score has been calculated per user the mentioned preference aggregation strategies can be used.
All of the aggregation functions mentioned in \autoref{subsec:Concept:SolutionGeneration:ScoringFunction} have one preference per product. For configuration where a preference for all characteristics exists there needs to be a function that combines the preferences of one user into her configuration score. After one score has been calculated per user the mentioned preference aggregation strategies can be used.
A simple scoring function approach is to use the the preference for each characteristic that is part of the configuration and then use the average. This approach is transparent because the preference of a user is directly translated into the score and no weighting is done. It means that a configuration score is simple to understand and to calculate. However, if needed, for example, to give one group member more power, it allows relative weighting. This can be done with preprocessing of preferences. Moreover, an approach like this ensures that through preprocessing feature weights can be added. It is therefore possible that a user gives different importances to features. Also, other means of weighting ratings are possible. For example the ratings of one group member who has more knowledge in an area can be increased by multiplication with a factor or alternatively the preferences for all other users can be decreased.
The formula for this rating function looks as follows: