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71 lines
4.6 KiB
TeX
71 lines
4.6 KiB
TeX
\chapter{Foundations}
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\label{ch:Foundations}
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\section{Recommender System}
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\label{sec:Foundations:RecommenderSystem}
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A recommender system is a system that gives individualized recommendations to users to guide them through a large space of objects \cite[~ p. 331]{burkeHybridRecommenderSystems2002}.
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There are several approaches to recommender systems presented in \cite{felfernigGroupRecommenderSystems2018}, these are: collaborative filtering, content-based filtering, critiquing-based filtering, constraint-based, hybrid recommendation.
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\subsection{Collaborative Filtering}
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In collaborative filtering a users rating for unknown items is predicted by finding similar users who have rated it. Their rating is used as prediction
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\cite[~ pp. 7, 8]{felfernigDecisionTasksBasic2018}.
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\subsection{Content-Based Filtering}
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Items and users are assigned to categories. Based on consumption and rating of items a user will have implicit ratings for categories. Predictions are now made based on a categories of the new item \cite[~ pp. 10, 11]{felfernigDecisionTasksBasic2018}.
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\subsection{Critiquing-Based Recommendation}
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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}.
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\subsection{Constraint-Based Recommendation}
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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 \cite[~ p. 12]{felfernigDecisionTasksBasic2018}
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\subsection{Hybrid Recommendation}
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A hybrid recommender combines different recommendation approaches to use the strengths of each individual one and to reduce effects of weaknesses \cite{burkeHybridRecommenderSystems2002}.
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\todo[inline]{add more detailed description of recommendation systems - do they need historicle data which conditions for usage are needed, strengths and weaknesses}
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\section{Group Recommender System}
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A group recommender system is a recommender system aimed at making recommendations for a group instead of a single user. To make recommendations group members preferences have to be aggregated. This can be done by either aggregating single user recommendations or by merging preferences of each user into a group preference model. Based on this model recommendation strategies as described in \ref{sec:Foundations:RecommenderSystem} can be used to generate recommendations \cite{jamesonRecommendationGroups2007}.
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\section{Product Configuration}
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\label{sec:Foundations:ProductConfiguration}
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Product configuration is a process consisting of a series of decision tasks whereby a product is constructed of components which interact with each other. During a configuration process no new components are created. Their interplay and specification is defined beforehand \cite[~ pp. 42, 43]{sabinProductConfigurationFrameworksa1998}.
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Formally a configuration problem can be specified as a \emph{constraint satisfaction problem (CSP)} \cite{tsangFoundationsConstraintSatisfaction1993} as
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\[
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CSP(V,D,C)
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\]
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where \( V = \{v_1,\dots, v_n\} \) is a set of variables, \( D = dom : V \mapsto X \) is a relation of variables and their corresponding domain definitions \( X \), and \( C = C_{PREF} \cup C_{KB} \) is a set of constraints with customer preferences \( C_{PREF} \) and configuration knowledge base \( C_{KB} \) \cite{felferningGroupBasedConfiguration2016, felfernigOpenConfiguration2014}.
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\section{Group-Based Product Configuration}
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\label{sec:Foundations:GroupBasedProductConfiguration}
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To define group-based product configuration we extend the definition (\ref{sec:Foundations:ProductConfiguration}) to
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\[
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C_{PREF} = \bigcup
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PREF_i \]
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with preferences of user \( i \) as \( PREF_i \) \cite{ felferningGroupBasedConfiguration2016}.
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\section{Group-Based Configuration-Solution}
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\label{sec:Foundations:GroupBasedConfigurationSolution}
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\ref{sec:Foundations:ProductConfiguration} and \ref{sec:Foundations:GroupBasedProductConfiguration} expand to a solution of a group-based configuration with the addition of variable assignments
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\[
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C_{CONF} = \bigcup_{v_i \in V} \{ v_i = x_i \}, \ x_i \in dom(v_i)
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\]
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and where \( C_{CONF} \cup C_{PREF} \cup C_{KB} \) is consistent \cite{ felferningGroupBasedConfiguration2016}.
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\todo[inline]{add group descision part, conflict resolution, types of conflict, differences in knowledge/decision power;
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group dynamics (e.g. biases in decision making, anchoring, etc.)
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Why are group decision interesting?}
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