From 776be58891bb4eba9634198578695ebc7c1b61f2 Mon Sep 17 00:00:00 2001 From: "hannes.kuchelmeister" Date: Tue, 12 Nov 2019 13:08:07 +0100 Subject: [PATCH] added three additional thesis to related work --- 02_Proposal/sections/20_related_work.tex | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/02_Proposal/sections/20_related_work.tex b/02_Proposal/sections/20_related_work.tex index b5b1444..07373a7 100644 --- a/02_Proposal/sections/20_related_work.tex +++ b/02_Proposal/sections/20_related_work.tex @@ -11,12 +11,12 @@ In his thesis \citeauthor{raabKollaborativeProduktkonfigurationEchtzeit2019} bui \citeauthor{rubinshteynEntwicklungHybridenRecommender2018} looks at different approaches to recommendation and implements a prototype with CAS Merlin Configurator which uses a hybrid recommender system. His prototype combines constraint-based filtering with collaborative filtering \cite{rubinshteynEntwicklungHybridenRecommender2018}. +\citeauthor{benzMoeglichkeitenIntelligenterEmpfehlungssysteme2017} uses a constraint based recommender that uses fuzzy logic to relax constraints and thereby reducing the amount of times where the recommender is unable to make recommendations. With his approach a product manager has direct influence on the recommendations. Rules for recommendations hereby are not automatically learned but only manually created and relate to predefined user interest categories \cite{benzMoeglichkeitenIntelligenterEmpfehlungssysteme2017}. -\todo[inline]{other related work at CAS} +\citeauthor{ullmannEntwurfUndUmsetzung2017} implements a recommendation engine that is able to estimate customer budgets, a k-nearest neighbour classifier for finding a base configuration and non-negative matrix factorization combines with nearest neighbour to find configurations for specific users \cite{ullmannEntwurfUndUmsetzung2017}. -\cite{benzMoeglichkeitenIntelligenterEmpfehlungssysteme2017} -\cite{ullmannEntwurfUndUmsetzung2017} -\cite{wetzelPersonalisierterUndLernender2017} +\citeauthor{wetzelPersonalisierterUndLernender2017} combines collaborative filtering and click-stream analysis. For collaborative filtering he implements three filtering algorithms: k-nearest neighbour, weighted majority voting and non-negative matrix factorization. Collaborative filtering is used to find configurations that are similar to the current configuration. Click-stream analysis is done by using n-grams and the Smith-Waterman algorithm. \citeauthor{wetzelPersonalisierterUndLernender2017} also tries to use click-stream data in combination with Markov chains to give recommendations on how configuration options should be ordered in a configuration form +\cite{wetzelPersonalisierterUndLernender2017}. \todo[inline]{other related work} \ No newline at end of file