@ARTICLE{SchirruBaumannMemmelDengel+10,
  author = {Rafael Schirru and Stephan Baumann and Martin Memmel and Andreas
	Dengel},
  title = {Extraction of Contextualized User Interest Profiles in Social Sharing
	Platforms},
  journal = {Journal of Universal Computer Science},
  year = {2010},
  volume = {16},
  pages = {2196--2213},
  number = {16},
  abstract = {Along with the emergence of theWeb 2.0, E-learning more often takes
	place in open environments such as wikis, blogs, and resource sharing
	platforms. Nowadays, many companies deploy social media technologies
	to foster the knowledge transfer in the enterprise. They offer Enterprise
	2.0 platforms where knowledge workers can share contents according
	to their different topics of interest.
	
	In this article we present an approach extracting contextualized user
	profiles in an enterprise resource sharing platform according to
	the users? different topics of interest. The system analyses the
	social annotations of each user?s preferred resources and identifies
	thematic groups. For every group a weighted term vector is derived
	that represents the respective topic of interest. Each user profile
	consists of several such vectors that way enabling recommendation
	lists with a high degree of inter-topic diversity as well as targeted
	context-sensitive recommendations.
	
	The proposed approach has been tested in our Enterprise 2.0 platform
	ALOE. A first evaluation has shown that the method is likely to identify
	reasonable user interest topics and that resource recommendations
	for these topics are widely appreciated by the users.},
  owner = {memmel},
  timestamp = {2010.12.06},
  url = {http://www.jucs.org/jucs_16_16/extraction_of_contextualized_user/}
}