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.
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Notes
- 1.
Note that the results reported here for LocalRank differ from the results in [4] as the experimental setups are different. First, our rating has 5 scales, which is intended to capture more detailed expertise level. Second, [4] only considers ideal ranking order for the top 10 results from LocalRank when calculating maximum possible (ideal) DCG@10, while we consider a much larger corpus.
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Niu, W., Liu, Z., Caverlee, J. (2016). LExL: A Learning Approach for Local Expert Discovery on Twitter. In: Ferro, N., et al. Advances in Information Retrieval. ECIR 2016. Lecture Notes in Computer Science(), vol 9626. Springer, Cham. https://doi.org/10.1007/978-3-319-30671-1_71
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DOI: https://doi.org/10.1007/978-3-319-30671-1_71
Publisher Name: Springer, Cham
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