Name
Abstract | ||
|---|---|---|
Crowdsourcing systems (CS) are platforms that enable a system or a user to publish tasks in order to be accomplished by others. Typically, a CS is a system where users, called workers, perform tasks using desktop computers. Recently, some CS have appeared with spatiotemporal tasks. Such tasks require a worker to be in a given location within a specific time-window to be accomplished. We propose and study here the usage of five heuristics for solving the NP-hard trajectory recommendation problem (TRP). In a TRP, the system recommends a trajectory to a worker that allows him to accomplish spatiotemporal tasks he has skill and/or affinity with, without exceeding his available time. Our experiments show that some of our heuristics are efficient alternatives for a heavy optimal approach providing trajectories with an average utility of about 60% of the optimal ones. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2837126.2837181 | MoMM |
Field | DocType | Citations |
Publication,Data mining,Crowdsourcing,Crowdsensing,Computer science,Heuristics,Artificial intelligence,Machine learning,Trajectory,Distributed computing | Conference | 2 |
PageRank | References | Authors |
0.39 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| André Sales Fonteles | 1 | 13 | 2.88 |
| Sylvain Bouveret | 2 | 7 | 1.51 |
| Jérôme Gensel | 3 | 3 | 1.42 |