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fix spelling of constraint

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hannes.kuchelmeister
2020-05-08 19:29:52 +02:00
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30_Thesis/sections/10_foundations.tex
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@@ -99,7 +99,7 @@ Using the example from \autoref{tab:Foundations:RecommenderSystem:MoviePreferenc
\label{tab:Foundations:RecommenderSystem:ContentBasedFilteringProfiles} \label{tab:Foundations:RecommenderSystem:ContentBasedFilteringProfiles}
\end{table} \end{table}
Advantages and disadvantages of basic recommendation techniques are listed in \autoref{tab:Foundations:RecommenderComparison}. The following subsections show advantages of content-based filtering over collaborative filtering and over constrained-based recommendation. Advantages and disadvantages of basic recommendation techniques are listed in \autoref{tab:Foundations:RecommenderComparison}. The following subsections show advantages of content-based filtering over collaborative filtering and over constraint-based recommendation.
\begin{table} \begin{table}
\begin{center} \begin{center}
@@ -160,9 +160,9 @@ Usually, the need for domain knowledge is a disadvantage. However, as product co
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. 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. Thus, malicious actors that try to manipulate the recommendation system do not decrease recommendation accuracy. The same is true for inaccurate preferences. For example, this occurs if a user's input into a system does not accurately reflect what they actually like. Last, content-based filtering does not depend on historic group preference accuracy. Thus, malicious actors that try to manipulate the recommendation system do not decrease recommendation accuracy. The same is true for inaccurate preferences. For example, this occurs if a user's input into a system does not accurately reflect what they actually like.
\subsubsection{Advantages over Constrained-Based Recommendation} \subsubsection{Advantages over Constraint-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 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. In constraint-based recommendation approaches it is possible that constraints lead to no possible solution \cite[~ p. 44]{felfernigAlgorithmsGroupRecommendation2018}. This then requires further techniques of constraint 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.
\section{Group Recommender System} \section{Group Recommender System}
\label{sec:Foundations:GroupRecommenderSystem} \label{sec:Foundations:GroupRecommenderSystem}