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Topic-based recommendations in enterprise social media sharing platforms

Rafael Schirru

RecSys '10: Proceedings of the fourth ACM conference on Recommender systems ACM Recommender Systems (RecSys-2010), September 26-30, Barcelona, Spain , Pages: 369-372 , ACM , 2010
Nowadays, many companies deploy social media technologies to foster the knowledge transfer in the enterprise. As the amount of available content in such systems grows, there is an increasing need for recommender systems that provide recommendations according to the knowledge workers' needs and preferences. We propose a topic-based recommender system for Enterprise 2.0 resource sharing platforms. The system identifies the knowledge workers' short-term and long-term topics of interest by applying algorithms from the domain of topic detection and tracking and generates recommendations with a high degree of inter-topic diversity.

Show BibTex:

@inproceedings {
       abstract = {Nowadays, many companies deploy social media technologies to foster the knowledge transfer in the enterprise. As the amount of available content in such systems grows, there is an increasing need for recommender systems that provide recommendations according to the knowledge workers' needs and preferences. We propose a topic-based recommender system for Enterprise 2.0 resource sharing platforms. The system identifies the knowledge workers' short-term and long-term topics of interest by applying algorithms from the domain of topic detection and tracking and generates recommendations with a high degree of inter-topic diversity.},
       number = {}, 
       month = {9}, 
       year = {2010}, 
       title = {Topic-based recommendations in enterprise social media sharing platforms}, 
       journal = {}, 
       volume = {}, 
       pages = {369-372}, 
       publisher = {ACM}, 
       author = {Rafael Schirru}, 
       keywords = {},
       url = {http://portal.acm.org/citation.cfm?id=1864708.1864793&coll=GUIDE&dl=GUIDE&type=series&idx=SERIES11503&part=series&WantType=Proceedings&title=RecSys&CFID=103739268&CFTOKEN=15303190#}
}