tud
Recommending in an Enterprise Social Media Stream without Explicit User Feedback
2013-10-25
[Electronic ed.]
4519974-7
Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden
prv
Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, Dresden
Medienzentrum
male
male
male
male
Social Media Streams allow users to share user-generated content as well as aggregate different streams into one single stream. Additional Enterprise Social Media Streams organize the stream messages into projects with different usage patterns compared to public collaboration platforms such as Twitter. The aggregated stream helps the user to access the information in one single place but also leads to an information overload. Here, a recommendation engine can help to distinguish between relevant and irrelevant information for the users.
In previous work we showed how features inferred from messages can predict relevant information and can be used to learn a user model. In this paper we show how this approach can be used in a productive enterprise social media stream application without using explicit user feedback. We develop a time binned evaluation measure which suits the scenario to steadily recommend messages of the stream. Finally, we evaluate our algorithm in different variations and show that it helps to identify relevant messages.
330
QR 760
Konferenz, GeNeMe 2013, Neue Medien, soziale Netzwerke, Vorschlagssysteme, Unternehmen
conference, new media, social media, stream recommender system, Social Media Stream
urn:nbn:de:bsz:14-qucosa-126221
39897201X
Technische Universität Dresden
pbl
Technische Universität Dresden, Dresden
Torsten
Lunze
aut
Philipp
Katz
aut
Dirk
Röhrborn
aut
Alexander
Schill
aut
eng
2013
urn:nbn:de:bsz:14-qucosa-125446
337
qucosa:26164Online Communities: Enterprise Networks, Open Education and Global Communication
T. Köhler & N. Kahnwald (Hrsg.), Online Communities: Enterprise Networks, Open Education and Global Communication: 16. Workshop GeNeMe ’13 Gemeinschaften in Neuen Medien, Dresden: TUDpress, ISBN: 978-3-944331-24-9, S. 337-346
born digital
MV
michaela.voigt@slub-dresden.de
in_proceeding
Service
GeNeMe 2013: 337-346