File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/INFOCOM.2017.8057112
- Scopus: eid_2-s2.0-85021301276
- WOS: WOS:000425232200169
- Find via
Supplementary
- Citations:
- Appears in Collections:
Conference Paper: Where are you from: Home location profiling of crowd sensors from noisy and sparse crowdsourcing data
Title | Where are you from: Home location profiling of crowd sensors from noisy and sparse crowdsourcing data |
---|---|
Authors | |
Keywords | Crowdsourcing Home Location Profiling Location Based Social Networks (LBSN) |
Issue Date | 2017 |
Citation | IEEE Conference on Computer Communications (IEEE INFOCOM 2017), Atlanta, GA, 1-4 May 2017. In IEEE INFOCOM 2017 - IEEE Conference on Computer Communications, 2017 How to Cite? |
Abstract | Crowdsourcing has emerged as an important data collection paradigm in participatory and human-centric sensing applications. While many crowdsourcing studies focus on sensing and recovering the status of the physical world, this paper investigates the problem of profiling the crowd sensors (i.e., humans). In particular, we study the problem of accurately inferring the home locations of people from the noisy and sparse crowdsourcing data they contribute. In this study, we propose a semi-supervised framework, Where Are You From (WAYF), to accurately infer the home locations of people by explicitly exploring the localness of people and the dependency between people based on their check-in behaviors under a rigorous analytical framework. We perform extensive experiments to evaluate the performance of our scheme and compared it to the state-of-the-art techniques using three real world data traces collected from Foursquare. The results showed the effectiveness of our scheme in accurately profiling the home locations of people. |
Persistent Identifier | http://hdl.handle.net/10722/308721 |
ISSN | 2023 SCImago Journal Rankings: 2.865 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Huang, Chao | - |
dc.contributor.author | Wang, Dong | - |
dc.contributor.author | Zhu, Shenglong | - |
dc.date.accessioned | 2021-12-08T07:49:59Z | - |
dc.date.available | 2021-12-08T07:49:59Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | IEEE Conference on Computer Communications (IEEE INFOCOM 2017), Atlanta, GA, 1-4 May 2017. In IEEE INFOCOM 2017 - IEEE Conference on Computer Communications, 2017 | - |
dc.identifier.issn | 0743-166X | - |
dc.identifier.uri | http://hdl.handle.net/10722/308721 | - |
dc.description.abstract | Crowdsourcing has emerged as an important data collection paradigm in participatory and human-centric sensing applications. While many crowdsourcing studies focus on sensing and recovering the status of the physical world, this paper investigates the problem of profiling the crowd sensors (i.e., humans). In particular, we study the problem of accurately inferring the home locations of people from the noisy and sparse crowdsourcing data they contribute. In this study, we propose a semi-supervised framework, Where Are You From (WAYF), to accurately infer the home locations of people by explicitly exploring the localness of people and the dependency between people based on their check-in behaviors under a rigorous analytical framework. We perform extensive experiments to evaluate the performance of our scheme and compared it to the state-of-the-art techniques using three real world data traces collected from Foursquare. The results showed the effectiveness of our scheme in accurately profiling the home locations of people. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE INFOCOM 2017 - IEEE Conference on Computer Communications | - |
dc.subject | Crowdsourcing | - |
dc.subject | Home Location Profiling | - |
dc.subject | Location Based Social Networks (LBSN) | - |
dc.title | Where are you from: Home location profiling of crowd sensors from noisy and sparse crowdsourcing data | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/INFOCOM.2017.8057112 | - |
dc.identifier.scopus | eid_2-s2.0-85021301276 | - |
dc.identifier.isi | WOS:000425232200169 | - |