A Learning Approach for Local Expert Discovery
dc.contributor.advisor | Caverlee, James | |
dc.creator | Liu, Zhijiao | |
dc.date.accessioned | 2016-05-04T13:22:52Z | |
dc.date.available | 2016-05-04T13:22:52Z | |
dc.date.created | 2015-12 | |
dc.date.issued | 2015-12-02 | |
dc.date.submitted | December 2015 | |
dc.identifier.uri | https://hdl.handle.net/1969.1/156496 | |
dc.description.abstract | Local experts are critical for many location-sensitive information needs, and yet there is a research gap in our understanding of the factors impacting who is recognized as a local expert and in methods for discovering local experts. Hence, this thesis: (i) proposes a geo-spatial learning-based framework, Local Expert Learning (LExL), for integrating multidimensional factors impacting local expertise, e.g. user-based, list-based, location-based and content-based features; (ii) accomplishes a comprehensive controlled study over AMT-labeled local experts on eight topics and in four cities, which not only leverages the candidates’ basic information, but also considers the location authority impacting a candidate’s expertise; and (iii) develops a prototype system, Local Experts Visualizing and Rating System (LEVRS), for visualizing and rating local experts. We find significant improvements (around 45% in precision and 50% in NDCG) of finding local experts compared to two state-of-the-art alternatives as well as evidence of the generalizability of the learned local expert ranking models to new topics and new locations. | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dc.subject | Local expert search | en |
dc.subject | Learning to rank | en |
dc.subject | Twitter data mining | en |
dc.title | A Learning Approach for Local Expert Discovery | en |
dc.type | Thesis | en |
thesis.degree.department | Computer Science and Engineering | en |
thesis.degree.discipline | Computer Science | en |
thesis.degree.grantor | Texas A & M University | en |
thesis.degree.name | Master of Science | en |
thesis.degree.level | Masters | en |
dc.contributor.committeeMember | Furuta, Richard | |
dc.contributor.committeeMember | Burkart, Patrick | |
dc.type.material | text | en |
dc.date.updated | 2016-05-04T13:22:52Z | |
local.etdauthor.orcid | 0000-0001-8439-4374 |
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Texas A&M University Theses, Dissertations, and Records of Study (2002– )