diff --git a/30_Thesis/sections/20_related_work.tex b/30_Thesis/sections/20_related_work.tex
index 4160f0e..b2d7841 100644
--- a/30_Thesis/sections/20_related_work.tex
+++ b/30_Thesis/sections/20_related_work.tex
@@ -26,6 +26,8 @@ This chapter discusses related work. The approaches presented are either group r
 \section{Recommender Systems for Configuration}
 \label{sec:Related_Work:RecommenderSystemsForConfiguration}
 \begin{description}[style=unboxed, leftmargin=0cm, font=\normalfont]
+    \item[\citeauthor{falknerRecommendationTechnologiesConfigurable2011} \cite{falknerRecommendationTechnologiesConfigurable2011}] provide an overview of recommendation approaches for configuration to improve usability of configuration systems. They look at feature recommender to recommend which features in a configuration would be useful to have and at value recommender for these features. Additionally they discuss approaches for ranking and recommending explanations for inconsistencies between customers requirements and product rules however they do not provide any recommendations and point towards further needed research.
+      
     \item[\citeauthor{rubinshteynEntwicklungHybridenRecommender2018} \cite{rubinshteynEntwicklungHybridenRecommender2018}] looks at different approaches to recommendation and implements a prototype with CAS Merlin Configurator which uses a hybrid recommender system. His prototype combines constraint-based filtering with collaborative filtering and achieves better results in terms of precision than non-hybrid systems using collaborative filtering and constraint-based recommendation. In terms of recall his hybrid does not surpass the high numbers of constraint-based recommendations but improves upon collaborative-filtering. 
 
     \item [\citeauthor{benzMoeglichkeitenIntelligenterEmpfehlungssysteme2017} \cite{benzMoeglichkeitenIntelligenterEmpfehlungssysteme2017}] uses a constraint based recommender that uses fuzzy logic to relax constraints and thereby reducing the amount of times where the recommender is unable to make recommendations. With his approach a product manager has direct influence on the recommendations. Rules for recommendations hereby are not automatically learned but only manually created and relate to predefined user interest categories.
@@ -33,7 +35,5 @@ This chapter discusses related work. The approaches presented are either group r
     \item [\citeauthor{ullmannEntwurfUndUmsetzung2017} \cite{ullmannEntwurfUndUmsetzung2017}] implements a recommendation engine that is able to estimate customer budgets, a k-nearest neighbour classifier for finding a base configuration and non-negative matrix factorization combined with nearest neighbour to find configurations for specific users. \par
 
     \item[\citeauthor{wetzelPersonalisierterUndLernender2017} \cite{wetzelPersonalisierterUndLernender2017}] combines collaborative filtering and click-stream analysis. For collaborative filtering he implements three filtering algorithms: k-nearest neighbour, weighted majority voting and non-negative matrix factorization. Collaborative filtering is used to find configurations that are similar to the current configuration. Click-stream analysis is done by using n-grams and the Smith-Waterman algorithm. \citeauthor{wetzelPersonalisierterUndLernender2017} also tries to use click-stream data in combination with Markov chains to give recommendations on how configuration options should be ordered in a configuration form but click-streams do not yield any improvements in terms of accuracy and recall.
-
-    \item[\citeauthor{falknerRecommendationTechnologiesConfigurable2011} \cite{falknerRecommendationTechnologiesConfigurable2011}] provide an overview of recommendation approaches for configuration to improve usability of configuration systems. They look at feature recommender to recommend which features in a configuration would be useful to have and at value recommender for these features. Additionally they discuss approaches for ranking and recommending explanations for inconsistencies between customers requirements and product rules however they do not provide any recommendations and point towards further needed research.
 \end{description}