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added three additional thesis to related work

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02_Proposal/sections/20_related_work.tex
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@@ -11,12 +11,12 @@ In his thesis \citeauthor{raabKollaborativeProduktkonfigurationEchtzeit2019} bui
\citeauthor{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 \cite{rubinshteynEntwicklungHybridenRecommender2018}. \citeauthor{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 \cite{rubinshteynEntwicklungHybridenRecommender2018}.
\citeauthor{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 \cite{benzMoeglichkeitenIntelligenterEmpfehlungssysteme2017}.
\todo[inline]{other related work at CAS} \citeauthor{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 combines with nearest neighbour to find configurations for specific users \cite{ullmannEntwurfUndUmsetzung2017}.
\cite{benzMoeglichkeitenIntelligenterEmpfehlungssysteme2017} \citeauthor{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
\cite{ullmannEntwurfUndUmsetzung2017} \cite{wetzelPersonalisierterUndLernender2017}.
\cite{wetzelPersonalisierterUndLernender2017}
\todo[inline]{other related work} \todo[inline]{other related work}