Abstract
As a key research issue in mobile crowdsensing (MCS), recent studies on task recommendation have begun to focus on recommending tasks to participants according to the learned participant preferences. The common drawbacks of these studies are that, on the one hand, the factors affecting participant preferences are predefined, which is not practical as the influential factors are quite complex and a full map of participant profiles needs to be preexisted. On the other hand, they do not consider how to update the recommendation dynamically. To overcome these drawbacks, a profile-free and real-time task recommendation method is proposed in this work. First, we apply the recommendation systems to MCS to realize profile-free task recommendations. Second, a participant-task-location tensor is constructed, based on which an improved tensor factorization method is presented to provide task recommendations for participants at a given location. Finally, we design a real-time update algorithm based on the idea of one update at a time to update task recommendation lists for participants in real time. Based on real-world trace data sets, extensive evaluations show that the proposed method has obvious advantages over other baselines in terms of accuracy and time cost.
| Original language | English |
|---|---|
| Pages (from-to) | 1311-1322 |
| Number of pages | 12 |
| Journal | IEEE Transactions on Computational Social Systems |
| Volume | 8 |
| Issue number | 6 |
| Early online date | 29 Apr 2021 |
| DOIs | |
| Publication status | Published - 1 Dec 2021 |
Bibliographical note
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Funding Information: This work was supported in part by the National Natural Science Foundation of China under Grant 61802257 and Grant 61602305 and in part by the Natural Science Foundation of Shanghai under Grant 18ZR1426000 and Grant 19ZR1477600.Keywords
- Crowdsensing
- Matrix decomposition
- Mobile crowdsensing (MCS)
- profile-free
- real-time
- Real-time systems
- Resource management
- Sensors
- Task analysis
- task recommendation.
- Tensors
ASJC Scopus subject areas
- Modelling and Simulation
- Social Sciences (miscellaneous)
- Human-Computer Interaction