<div class="csl-bib-body">
<div class="csl-entry">Neumeyer, M. (2020). <i>Analyse einer dynamischen Sammlung von Zeitungsartikeln mit inhaltsbasierten Methoden</i> [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2020.70883</div>
</div>
-
dc.identifier.uri
https://doi.org/10.34726/hss.2020.70883
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/16187
-
dc.description.abstract
The consumption of news changed throughout the last decades, a huge amount of articles is available at any time in the internet. Consumers therefore need help to find articles that might be relevant for them, as they are not able to scan through all offered articles themselves. This led to the emergence of news recommender systems.The way in which these systems choose articles that might be relevant varies vastly. One kind of methods are the content-based methods, which use only the written content of news articles and build relations between articles for the recommendations based on it. In contrast to collaborative filtering methods, which also use demographic data and previously gathered interests of users.In this work we analyze and compare current state of the art methods for content-based recommendations of news articles.The focus of the comparison will be on two main points. On the one hand is the ability to analyze a dynamic corpus. This includes both the possibility to include new articles to an existing model, as well as finding trends within the found topics or keywords of a model. On the other hand comes the diversity and serendipity of recommendations. Most comparisons of recommender systems put the focus on the accuracy of recommendations,instead this thesis will put the focus on diversity and serendipity to further improve the quality of recommendations. The conclusion of this comparison is, that every method has its strengths and weaknesses. No method could be found that exceeds all other methods in all aspects that were considered.
en
dc.language
English
-
dc.language.iso
en
-
dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
-
dc.subject
Computerlinguistik
de
dc.subject
CL
de
dc.subject
Vergleich
de
dc.subject
inhaltsbasiert
de
dc.subject
Nachrichten
de
dc.subject
Zeitungsartikel
de
dc.subject
Empfehlung
de
dc.subject
Empfehlungssystem
de
dc.subject
Nachrichtenempfehlung
de
dc.subject
Diversität
de
dc.subject
Überraschung
de
dc.subject
positive Überraschung
de
dc.subject
glücklicher Zufall
de
dc.subject
Schlagwort
de
dc.subject
Schlagwortfindung
de
dc.subject
Trend
de
dc.subject
Trendanalyse
de
dc.subject
Dynamischer Textkorpus
de
dc.subject
natural language processing
en
dc.subject
NLP
en
dc.subject
comparison
en
dc.subject
content-based
en
dc.subject
news
en
dc.subject
news article
en
dc.subject
news recommender
en
dc.subject
recommendation
en
dc.subject
news recommender system
en
dc.subject
beyond accuracy
en
dc.subject
diversity
en
dc.subject
surprise
en
dc.subject
serendipity
en
dc.subject
keyword
en
dc.subject
keyword detection
en
dc.subject
trend
en
dc.subject
trend analysis
en
dc.subject
dynamic corpus
en
dc.title
Analyse einer dynamischen Sammlung von Zeitungsartikeln mit inhaltsbasierten Methoden
en
dc.title.alternative
Analysis of a dynamic collection of news articles with content-based methods
de
dc.type
Thesis
en
dc.type
Hochschulschrift
de
dc.rights.license
In Copyright
en
dc.rights.license
Urheberrechtsschutz
de
dc.identifier.doi
10.34726/hss.2020.70883
-
dc.contributor.affiliation
TU Wien, Österreich
-
dc.rights.holder
Markus Neumeyer
-
dc.publisher.place
Wien
-
tuw.version
vor
-
tuw.thesisinformation
Technische Universität Wien
-
dc.contributor.assistant
Neidhardt, Julia
-
tuw.publication.orgunit
E194 - Institut für Information Systems Engineering