Stage-oe-small.jpg

Inproceedings3766: Unterschied zwischen den Versionen

Aus Aifbportal
Wechseln zu:Navigation, Suche
 
Zeile 13: Zeile 13:
 
{{Inproceedings
 
{{Inproceedings
 
|Referiert=Ja
 
|Referiert=Ja
|Title=Towards Recommending Interesting Content in Document Archives
+
|Title=Towards Recommending Interesting Content in News Archives
 
|Year=2018
 
|Year=2018
 
|Booktitle=Proceedings of the 20th International Conference on Asia-Pacific Digital Libraries (ICADL 2018)
 
|Booktitle=Proceedings of the 20th International Conference on Asia-Pacific Digital Libraries (ICADL 2018)
Zeile 23: Zeile 23:
 
|Link=https://link.springer.com/chapter/10.1007/978-3-030-04257-8_13
 
|Link=https://link.springer.com/chapter/10.1007/978-3-030-04257-8_13
 
|Forschungsgruppe=Web Science
 
|Forschungsgruppe=Web Science
 +
}}
 +
{{Forschungsgebiet Auswahl
 +
|Forschungsgebiet=Information Retrieval
 +
}}
 +
{{Forschungsgebiet Auswahl
 +
|Forschungsgebiet=Digitale Bibliotheken
 
}}
 
}}

Aktuelle Version vom 16. Mai 2021, 15:47 Uhr


Towards Recommending Interesting Content in News Archives


Towards Recommending Interesting Content in News Archives



Published: 2018

Buchtitel: Proceedings of the 20th International Conference on Asia-Pacific Digital Libraries (ICADL 2018)
Verlag: Springer

Referierte Veröffentlichung

BibTeX

Kurzfassung
Recently, many archival news article collections have been made available to wide public. However, such collections are typically large, making it difficult for users to find content they would be interested in. Furthermore, archived news articles tend to be perceived by ordinary users as having rather weak attractiveness and being obsolete or uninteresting. In this paper, we propose the task of finding interesting content from news archives and introduce two simple methods for it. Our approach recommends interesting content by comparing the information written in the past with the one from the present.

Download: Media:RecInteresting_ICADL2018.pdf
Weitere Informationen unter: Link



Forschungsgruppe

Web Science


Forschungsgebiet

Information Retrieval, Digitale Bibliotheken