changed he/she to singular they

30_Thesis/sections/10_foundations.tex

@@ -157,14 +157,14 @@ Advantages and disadvantages of basic recommendation techniques are listed in \a
 \subsubsection{Advantages over Collaborative Filtering}
 
 Collaborative filtering has several issues that content-based filtering doesn't have. According to \citeauthor{likaFacingColdStart2014} \cite{likaFacingColdStart2014} the \emph{cold start problem} is one of the well-known problems of recommender systems. It occurs when there is sparse information for users or items. In the case of collaborative filtering this issue concerns for both items and users. Content-based filtering does not have that issue with items as items are classified based on similarity to other items. The user cold start problem however still persists when a new user has not yet rated any items. Therefore, no similar items can be recommended. 
-Another common issue is the \emph{grey sheep problem}. \citeauthor{grasIdentifyingGreySheep2016} \cite{grasIdentifyingGreySheep2016}. Collaborative filtering approaches assume that users that are similar, have similar preferences. A user that is not similar to any of the current user or community of users fail that assumption. Therefore, good recommendations cannot be made. These users are called \emph{grey sheep users}. Item-based filtering does not have this issue as a user's preference is directly used to find similar items to the ones she liked, not similar users.
+Another common issue is the \emph{grey sheep problem}. \citeauthor{grasIdentifyingGreySheep2016} \cite{grasIdentifyingGreySheep2016}. Collaborative filtering approaches assume that users that are similar, have similar preferences. A user that is not similar to any of the current user or community of users fail that assumption. Therefore, good recommendations cannot be made. These users are called \emph{grey sheep users}. Item-based filtering does not have this issue as a user's preference is directly used to find similar items to the ones they like, not similar users.
 Usually, the need for domain knowledge is a disadvantage. However, as product configuration already has domain knowledge baked in to describe features and how they can be combined, this is not a disadvantage and can even be seen as an advantage. Therefore, domain knowledge can directly be used and does not first need to be learned indirectly.
 Additionally, a collaborative filtering approach spans a larger comparison space, based on preferences, compared to content-based filtering that only uses the item attributes. Thus, for applications with a large solution space, reliance on product features instead of user similarity should be considered.
-Last, content-based filtering does not depend on historic group preference accuracy. Therefore, malicious actors that try to manipulate the recommendation system do not decrease recommendation accuracy. The same is true for inaccurate preferences. For example if a user's input into a system does not accurately reflect what she actually likes.
+Last, content-based filtering does not depend on historic group preference accuracy. Therefore, malicious actors that try to manipulate the recommendation system do not decrease recommendation accuracy. The same is true for inaccurate preferences. For example if a user's input into a system does not accurately reflect what they actually like.
 
 \subsubsection{Advantages over Constrained-Based Recommendation}
 
-In constrained-based recommendation approaches it is possible that constraints lead to no possible solution \cite[~ p. 44]{felfernigAlgorithmsGroupRecommendation2018}. This then requires further techniques of constrained relaxing and a user is faced with the situation that he has to search for constraints which fulfil less strict requirements. Moreover, in groups a constraint-based approach has to deal with contrary user constraints. Therefore, diverse groups could have issues with it.
+In constrained-based recommendation approaches it is possible that constraints lead to no possible solution \cite[~ p. 44]{felfernigAlgorithmsGroupRecommendation2018}. This then requires further techniques of constrained relaxing and a user is faced with the situation that they have to search for constraints which fulfil less strict requirements. Moreover, in groups a constraint-based approach has to deal with contrary user constraints. Therefore, diverse groups could have issues with it.
 
 \subsection{Hybrid Recommendation}
 A hybrid recommender combines different recommendation approaches to use the strengths of each individual one and to reduce effects of weaknesses \cite{burkeHybridRecommenderSystems2002}.