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2016
Springer

LExL: A Learning Approach for Local Expert Discovery on Twitter

4 years 3 months ago
LExL: A Learning Approach for Local Expert Discovery on Twitter
Abstract. In this paper, we explore a geo-spatial learning-to-rank framework for identifying local experts. Three of the key features of the proposed approach are: (i) a learning-based framework for integrating multiple factors impacting local expertise that leverages the fine-grained GPS coordinates of millions of social media users; (ii) a location-sensitive random walk that propagates crowd knowledge of a candidate’s expertise; and (iii) a comprehensive controlled study over AMT-labeled local experts on eight topics and in four cities. We find significant improvements of local expert finding versus two state-of-the-art alternatives.
Wei Niu, Zhijiao Liu, James Caverlee
Added 02 Apr 2016
Updated 02 Apr 2016
Type Journal
Year 2016
Where ECIR
Authors Wei Niu, Zhijiao Liu, James Caverlee
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