|Title:||Analysis of a dynamic collection of news articles with content-based methods||Language:||English||Authors:||Neumeyer, Markus||Keywords:||Computerlinguistik; CL; Vergleich; inhaltsbasiert; Nachrichten; Zeitungsartikel; Empfehlung; Empfehlungssystem; Nachrichtenempfehlung; Diversität; Überraschung; positive Überraschung; glücklicher Zufall; Schlagwort; Schlagwortfindung; Trend; Trendanalyse; Dynamischer Textkorpus
natural language processing; NLP; comparison; content-based; news; news article; news recommender; recommendation; news recommender system; beyond accuracy; diversity; surprise; serendipity; keyword; keyword detection; trend; trend analysis; dynamic corpus
|Advisor:||Werthner, Hannes||Assisting Advisor:||Neidhardt, Julia||Issue Date:||2020||Number of Pages:||97||Qualification level:||Diploma||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.
|DOI:||10.34726/hss.2020.70883||Library ID:||AC16072124||Organisation:||E194 - Institut für Information Systems Engineering||Publication Type:||Thesis
|Appears in Collections:||Hochschulschrift | Thesis|
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