Towards Near Real-Time Social Recommendations for the Enterprise
Benjamin Heitmann, Maciej Dabrowski, Conor Hayes, Keith Griffin · Intelligent systems reference library · 2015
The widespread use of social platforms in contemporary organizations leads to the generation of large amounts of content shared through various social tools. This information is distributed and often unstructured, making it difficult to fully exploit its value in an enterprise context. While Semantic Web technologies allow for publishing meaningful and structured data, major challenges include: (1) real-time integration of distributed social data, and (2) content personalization to identify relevant pieces of information and present them to users to limit the information overload. We propose to combine Semantic Web technologies with standardized transport protocols, such as XMPP, to provide an efficient and open source layer for aggregation of distributed social data in an enterprise. In addition, we propose a personalisation approach, which is able to provide filtered and personalised access on top of such distributed social data. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.