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- W3084869987 abstract "In mobile crowd sensing, whether or not the results of task allocation are consistent with the user’s preferences will greatly affect the efficiency of task execution. Hence, recent studies have begun to focus on the user’s preference and, based on it, provide a task recommendation list to users. However, these studies can neither accurately mine users’ preferences nor consider the dynamic updating of users’ preferences. In this work, firstly, a matrix factorization method is employed to mine users’ preferences for tasks in mobile crowd sensing system. Secondly, a method of generating a task recommendation list is designed to provide task recommendations to users according to their preferences for tasks. Finally, a real-time update algorithm is proposed to make the task recommendation list adapt to the users’ preferences changed in real time, so that the user’s task recommendation list can be updated rapidly each time the user’s performance data changes, and the real-time task recommendation can be realized. Extended evaluations based on real world mobility traces show that, compared with the existing models, our model has significant advantages in accuracy and the time cost." @default.
- W3084869987 created "2020-09-21" @default.
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- W3084869987 date "2020-01-01" @default.
- W3084869987 modified "2023-10-16" @default.
- W3084869987 title "A Real-Time Recommendation Algorithm for Task Allocation in Mobile Crowd Sensing" @default.
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- W3084869987 doi "https://doi.org/10.1007/978-3-030-59016-1_53" @default.
- W3084869987 hasPublicationYear "2020" @default.
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