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added todos instead of uncommenting for critique based recommendation

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25_Outline/sections/20_foundations.tex
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@@ -94,10 +94,12 @@ Collaborative Filtering can not only be done using users, it can also be item-ba
\autoref{tab:Foundations:RecommenderSystem:MoviePreferences} shows an example rating matrix. A simple user-based way to calculate rating would be now to use a k-nearest neighbour (kNN) algorithm and then take the average of those ratings. Using this method with $k := 2$ and euclidean distance our closest neighbours are \textit{Lucy} and \textit{Diane} therefore giving us a predicted rating of $4$. If we use item based illustration instead, we will try to find similar items based on the users rating. An example of similar items here would be \textit{Forest Gump} and \textit{Wall-E} as John and Lucy each have given them the sane rating and Eric's rating is off by one. Using again kNN with $k := 2$ we find that \textit{Forest Gump} and \textit{Wall-E} are the most similar to \textit{Titanic} thereby having a predicted rating of $4.5$. \autoref{tab:Foundations:RecommenderSystem:MoviePreferences} shows an example rating matrix. A simple user-based way to calculate rating would be now to use a k-nearest neighbour (kNN) algorithm and then take the average of those ratings. Using this method with $k := 2$ and euclidean distance our closest neighbours are \textit{Lucy} and \textit{Diane} therefore giving us a predicted rating of $4$. If we use item based illustration instead, we will try to find similar items based on the users rating. An example of similar items here would be \textit{Forest Gump} and \textit{Wall-E} as John and Lucy each have given them the sane rating and Eric's rating is off by one. Using again kNN with $k := 2$ we find that \textit{Forest Gump} and \textit{Wall-E} are the most similar to \textit{Titanic} thereby having a predicted rating of $4.5$.
However this simple similarity and prediction function does not take into account different distances. For example Lucy's ratings are more similar compared to Eric's than Diane's but Diane's and Lucy's rating is valued the same amount. However this simple similarity and prediction function does not take into account different distances. For example Lucy's ratings are more similar compared to Eric's than Diane's but Diane's and Lucy's rating is valued the same amount.
%\subsection{Critiquing-Based Recommendation} \todo[inline]{
%Items are recommended and a user can then critique on attributes of the recommendation. Based on that a similar item which does not have those critiques can be recommended. User preferences are implicitly collected this way \cite{knijnenburgEachHisOwn2011}. Critiquing-Based Recommendation %\subsection{Critiquing-Based Recommendation}
% Items are recommended and a user can then critique on attributes of the recommendation. Based on that a similar item which does not have those critiques can be recommended. User preferences are implicitly collected this way \cite{knijnenburgEachHisOwn2011}.
%With a critique based approach Eric sees a suggestion of watching \textit{The Island} and its attributes. He then can say that he finds this movie has too much action. The critique based recommender will now present a movie that has similar attributes as \textit{The Island} but with less action. For example \textit{Titanic} could be the next suggestion.
With a critique based approach Eric sees a suggestion of watching \textit{The Island} and its attributes. He then can say that he finds this movie has too much action. The critique based recommender will now present a movie that has similar attributes as \textit{The Island} but with less action. For example \textit{Titanic} could be the next suggestion.
}
\subsection{Constraint-Based Recommendation} \subsection{Constraint-Based Recommendation}
Hereby filter rules are defined which filter out items that don't fulfil specified rules. A user models their requirements with these rules and thereby gets a list of recommended items. This approach requires deep knowledge about a product because it needs a detailed description of features \cite[~ p. 12]{felfernigDecisionTasksBasic2018}. Hereby filter rules are defined which filter out items that don't fulfil specified rules. A user models their requirements with these rules and thereby gets a list of recommended items. This approach requires deep knowledge about a product because it needs a detailed description of features \cite[~ p. 12]{felfernigDecisionTasksBasic2018}.
@@ -148,8 +150,9 @@ Using our example from \autoref{tab:Foundations:RecommenderSystem:MoviePreferenc
\item No dependence of historic group preference accuracy \item No dependence of historic group preference accuracy
\end{itemize} \end{itemize}
%\subsubsection{Advantages over Critique-Based Recommendation} \todo[inline]{
%\todo[inline]{ fill out section} Advantages over Critique-Based Recommendation %\subsubsection{Advantages over Critique-Based Recommendation
}
\subsubsection{Advantages over Constrained-Based Recommendation} \subsubsection{Advantages over Constrained-Based Recommendation}
@@ -162,7 +165,6 @@ Using our example from \autoref{tab:Foundations:RecommenderSystem:MoviePreferenc
\begin{table} \begin{table}
%\todo[inline]{ add critique based recommendation and check sources}
\begin{center} \begin{center}
\begin{tabularx}{\columnwidth}{X|X|X} \begin{tabularx}{\columnwidth}{X|X|X}
\hline \hline
@@ -199,14 +201,7 @@ Using our example from \autoref{tab:Foundations:RecommenderSystem:MoviePreferenc
\item Requires domain knowledge \item Requires domain knowledge
\end{itemize} \\ \end{itemize} \\
\hline \hline
%Critique-Based Recommendation \todo[inline]{Critique-Based Recommendation } & \todo[inline]{ todo: positives } & \todo[inline]{ todo: negatives } \\
%& \begin{itemize}
% \item todo
% \end{itemize}
%& \begin{itemize}
% \item todo
% \end{itemize} \\
%\hline
Constraint-Based Recommendation Constraint-Based Recommendation
& \begin{itemize} & \begin{itemize}
\item Transparent \item Transparent