Context-aware services based on spatio-temporal zoning and crowdsourcing.
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| Title: | Context-aware services based on spatio-temporal zoning and crowdsourcing. |
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| Authors: | Ahmad, Akhlaq, Rahman, Md. Abdur, Ridza Wahiddin, Mohamed, Ur Rehman, Faizan, Khelil, Abdelmajid, Lbath, Ahmed |
| Source: | Behaviour & Information Technology. Jul2018, Vol. 37 Issue 7, p736-760. 25p. 10 Color Photographs, 4 Diagrams, 1 Chart, 6 Graphs, 2 Maps. |
| Subjects: | Computer networks, Customer satisfaction, Mathematical models, Research methodology, Surveys, Systems design, User interfaces, World Wide Web, Theory, Smartphones, Crowdsourcing |
| Abstract: | Crowdsourcing offers great opportunities to recognise user context and prescribe relevant services for both offline and real-time activities. In this work, we present a zoning model that leverages spatio-temporal dimensions and then employs different contexts to recommend necessary customised services. The context model takes into consideration three context sets: fully restricted, fully unrestricted and semi-restricted with respect to both spatial and temporal dimensions. As a proof of concept, we apply this zoning model in a scenario where a very large crowd get together to perform spatio-temporal activities. The user context of the heterogeneous crowd is captured using the carried smartphones, i.e. via crowdsourcing. Depending on the context sets and zone, the system can recommend a set of services to each user. The system has been deployed since 2014 to support the spatio-temporal activities of a very large crowd. We present our implementation details and the user feedback, which is very encouraging. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | Crowdsourcing offers great opportunities to recognise user context and prescribe relevant services for both offline and real-time activities. In this work, we present a zoning model that leverages spatio-temporal dimensions and then employs different contexts to recommend necessary customised services. The context model takes into consideration three context sets: fully restricted, fully unrestricted and semi-restricted with respect to both spatial and temporal dimensions. As a proof of concept, we apply this zoning model in a scenario where a very large crowd get together to perform spatio-temporal activities. The user context of the heterogeneous crowd is captured using the carried smartphones, i.e. via crowdsourcing. Depending on the context sets and zone, the system can recommend a set of services to each user. The system has been deployed since 2014 to support the spatio-temporal activities of a very large crowd. We present our implementation details and the user feedback, which is very encouraging. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 0144929X |
| DOI: | 10.1080/0144929X.2018.1476586 |
