On the recommending of citations for research papers
CSCW '02 Proceedings of the 2002 ACM conference on Computer supported cooperative work
Ontological user profiling in recommender systems
ACM Transactions on Information Systems (TOIS)
Research Paper Recommender Systems: A Random-Walk Based Approach
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Discovering User Profiles from Semantically Indexed Scientific Papers
From Web to Social Web: Discovering and Deploying User and Content Profiles
Combining learning and word sense disambiguation for intelligent user profiling
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Homophily in the Digital World: A LiveJournal Case Study
IEEE Internet Computing
Social networks and interest similarity: the case of CiteULike
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Using self-defined group activities for improvingrecommendations in collaborative tagging systems
Proceedings of the fourth ACM conference on Recommender systems
Research paper recommender systems: a subspace clustering approach
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
Interweaving public user profiles on the web
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
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In the last years, hundreds of social networks sites have been launched with both professional (e.g., LinkedIn) and non-professional (e.g., MySpace, Facebook) orientations. This resulted in a renewed information overload problem, but it also provided a new and unforeseen way of gathering useful, accurate and constantly updated information about user interests and tastes. Content-based recommender systems can leverage the wealth of data emerging by social networks for building user profiles in which representations of the user interests are maintained. The idea proposed in this paper is to extract content-based user profiles from the data available in the LinkedIn social network, to have an image of the users' interests that can be used to recommend interesting academic research papers. A preliminary experiment provided interesting results which deserve further attention.