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