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Communication Dans Un Congrès Année : 2015

Heuristics for Task Recommendation in Spatiotemporal Crowdsourcing Systems

André Sales Fonteles
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Jérôme Gensel

Résumé

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.
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Dates et versions

hal-01463421 , version 1 (09-02-2017)

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Citer

André Sales Fonteles, Sylvain Bouveret, Jérôme Gensel. Heuristics for Task Recommendation in Spatiotemporal Crowdsourcing Systems. Proceedings of the 13th International Conference on Advances in Mobile Computing and Multimedia, Dec 2015, Brussels, Belgium. ⟨10.1145/2837126.2837181⟩. ⟨hal-01463421⟩
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