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Finding co-solvers on Twitter, with a little help from linked data

Stankovic, Milan and Rowe, Matthew and Laublet, Philippe (2012) Finding co-solvers on Twitter, with a little help from linked data. In: The semantic web. Lecture Notes in Computer Science . Springer Verlag, Berlin, pp. 39-55. ISBN 978-3-642-30283-1

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Abstract

In this paper we propose a method for suggesting potential collaborators for solving innovation challenges online, based on their competence, similarity of interests and social proximity with the user. We rely on Linked Data to derive a measure of semantic relatedness that we use to enrich both user profiles and innovation problems with additional relevant topics, thereby improving the performance of co-solver recommendation. We evaluate this approach against state of the art methods for query enrichment based on the distribution of topics in user profiles, and demonstrate its usefulness in recommending collaborators that are both complementary in competence and compatible with the user. Our experiments are grounded using data from the social networking service Twitter.com.

Item Type: Contribution in Book/Report/Proceedings
Uncontrolled Keywords: Linked Data ; Twitter ; Collaborator Recommendation
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 58603
Deposited By: ep_importer_pure
Deposited On: 25 Sep 2012 09:43
Refereed?: No
Published?: Published
Last Modified: 15 Aug 2013 12:14
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/58603

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