fix errors

30_Thesis/sections/80_conclusion.tex

@@ -1,17 +1,17 @@
 \chapter{Conclusion}
 \label{ch:Conclusion}
 
-This chapter gives a summary about the thesis, discusses limitations and gives and outlook on possible further research.
+This chapter gives a summary about the thesis. It describes the exact steps that were undertaken, starting with an overview of the foundations regarding group recommenders and configuration. Furthermore, a concept for a recommender for group-based configuration is introduced, implemented as prototype and evaluated. Last, limitations of this thesis and the recommender system are given and further research is proposed.
 
 \section{Summary}
 \label{sec:Conclusion:Summary}
 
 
 To summarise, this thesis was motivated by the research area of group-based configuration gaining more traction. As group decisions come with many problems and biases, recommender systems are used  to help with group decisions. This avoids mistakes and helps with reproducibility of successful group decisions. For group-based configurations there has not been any research on recommenders. The research question of this thesis therefore was the following: "How can a group recommender translate individual preferences into recommendations that improve the overall satisfaction of group members while considering constraints given by the configuration state?". This thesis answers the research questions by proposing a concept, implementing it as a prototype and evaluating it. Thereby the viability of such a system and such an approach is shown.
-First, the thesis introduces foundations about product configuration and extends them to group-based product configuration. Next, recommender systems are introduced and three basic approaches, collaborative filtering, content-based filtering and constraint based recommendation, are compared. Last, the foundations chapter gives an introduction into group recommendation.
+First, the thesis introduces foundations about product configuration and extends them to group-based product configuration. Next, recommender systems are introduced and three basic approaches, collaborative filtering, content-based filtering and constraint based recommendation, are compared. Last, the \hyperref[ch:Foundations]{foundations chapter} gives an introduction into group recommendation.
-Second, a concept for an item-based recommender for group-based configuration is introduced. This concepts uses a database of finished configurations to chose a configuration that fits best to the group. Each users preferences are used to assign a score to a configuration. The score of all group members is then aggregated into a group score and the best configuration from the database is recommended. The recommender also gives penalties for deviating from the current configuration state.
+After the \hyperref[ch:Foundations]{foundations chapter}, a concept for an item-based recommender for group-based configuration is introduced. This concepts uses a database of finished configurations to chose a configuration that fits best to the group. Each users preferences are used to assign a score to a configuration. The score of all group members is then aggregated into a group score and the best configuration from the database is recommended. The recommender also gives penalties for deviating from the current configuration state.
-Third, the concept is implemented as an open source microservice which is integrated into an already existing group-based configurator.
+Later, the concept is implemented as an open source microservice which is integrated into an already existing group-based configurator.
-Last, an offline metric for satisfaction is introduced and it is used for evaluation. Three group types are evaluated, homogenous groups, random groups and heterogeneous groups. Overall, the evaluation shows the recommender yields good results for groups and help group form a compromise. Satisfaction among group members is increased. A simple item-based approach therefore,  already improves group decisions by finding good compromises. This is also the case when the knowledge of the recommender is limited.
+Last, an offline metric for satisfaction is introduced and it is used for evaluation. Three group types are evaluated, homogenous groups, random groups and heterogeneous groups. Overall, the evaluation shows the recommender yields good results for groups and helps groups to form a compromise. Satisfaction among group members is increased overall. A simple item-based approach therefore,  already improves group decisions by finding good compromises. This is also the case when the knowledge of the recommender is limited.
 
 \section{Limitations and Further Research}
 \label{sec:Conclusion:LimitationsFurtherResearch}