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 \tableofcontents
 
 \newpage
+
 \section{Research Gap}
 
-There exists research on group recommenders and research on recommenders for configuration but there does not exists much research on recommendation for group configuration. An approach for group recommenders is content-based filtering. This approach is used also in recommenders for configuration. That is why adapting this approaches to suit the use case of group recommenders for configuration will be analysed in this thesis.
+There does not exists much research on recommendation for group configuration, however it is comprised of two different areas of research, recommenders for groups and recommenders for configuration.
+The existing literature on recommenders for groups is extensive with many different approaches and domains \cite{delicResearchMethodsGroup2016, chenInterfaceInteractionDesign2011, atasItemRecommendationUsing2017, jamesonRecommendationGroups2007, chenEmpatheticonsDesigningEmotion2014, liuCGSPAComprehensiveGroup2019}. \citeauthor{felfernigGroupRecommenderSystems2018} give an overview about basic approaches \cite{felfernigGroupRecommenderSystems2018}.
+In are of product configuration research about recommender systems is undertaken as well \cite{pereiraFeaturebasedPersonalizedRecommender2016, scholzConfigurationbasedRecommenderSystem2017, scholzEffectsDecisionSpace2017}.
+Group configuration is a more specialized sub field of configuration therefore less research exists on that and because of that there has not been much research on recommenders that are for group recommendation. 
 
-Commonly for content-based recommenders categories based on content are created and a separate user or group profile is generated based on the preferences of whole items. For configuration recommenders however this would create additional modelling or content grouping workload, therefore in this thesis it is proposed to use attributes of a configuration as distinguishing categories.
+%Commonly for content-based recommenders categories based on content are created and a separate user or group profile is generated based on the preferences of whole items. For configuration recommenders however this would create additional modelling or content grouping workload, therefore in this thesis it is proposed to use attributes of a configuration as distinguishing categories.
 
 \section{Recommender Systems}