@INPROCEEDINGS{SchirruBaumannMemmelDengel+11,
  author = {Rafael Schirru and Stephan Baumann and Martin Memmel and Andreas
	Dengel},
  title = {Topic-Based Recommendations for Enterprise 2.0 Resource Sharing Platforms},
  booktitle = {Knowledge-Based and Intelligent Information and Engineering Systems},
  year = {2011},
  editor = {Andreas Koenig and Andreas Dengel and Knut Hinkelmann and Koichi
	Kise and Robert J. Howlett and Lakhmi C. Jain},
  volume = {6881},
  series = {Lecture Notes in Computer Science},
  pages = {495--504},
  month = {September},
  publisher = {Springer},
  abstract = {Companies increasingly often deploy social media technologies to foster
	the knowledge transfer between employees. As the amount of resources
	in such systems is usually large there is a need for recommender
	systems that provide personalized information access. Traditional
	recommender systems suffer from sparsity issues in such environments
	and do not take the users? different topics of interest into account.
	We propose a topic-based recommender system tackling these issues.
	Our approach applies algorithms from the domain of topic detection
	and tracking on the metadata profiles of the users? preferred resources
	to identify their interest topics. Every topic is represented as
	a weighted term vector that can be used to retrieve unknown, relevant
	resources matching the users? topics of interest. An evaluation of
	the approach has shown that our method retrieves on-topic resources
	with a high precision.},
  isbn = {978-3-642-23850-5},
  location = {Heidelberg},
  owner = {memmel}
}