Abstract
Social media streams can be used for aggregating heterogeneous information sources into a single representation. In Enterprise Social Media Streams, employees interact with the stream and with other employees producing a constantly growing amount of new information. For avoiding an information overload, a recommendation engine must help the user to filter important information. This paper uses a Stream Recommender System (SRS) and presents an algorithm for an SRS to work within an enterprise context. The algorithms makes use of different social media specific features, including a feature that maintains a content-based user model. The algorithm has been evaluated against ratings, which have been collected within an existing productive Enterprise 2.0 system.
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Lunze, T., Katz, P., Röhrborn, D., Schill, A. (2013). Stream-Based Recommendation for Enterprise Social Media Streams. In: Abramowicz, W. (eds) Business Information Systems. BIS 2013. Lecture Notes in Business Information Processing, vol 157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38366-3_15
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DOI: https://doi.org/10.1007/978-3-642-38366-3_15
Publisher Name: Springer, Berlin, Heidelberg
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