Abstract
With the developments of mobile services, mobilecrowdsourcing systems are attracting more and more attention. How to recommend user-preferred and trustful tasks for usersis an important issue to improve efficiency of mobile crowd-sourcing systems. This paper proposes a task recommendationmodel for mobile crowdsourcing systems based on dwell-time. Considering both user similarity and task similarity, the recom-mendation probabilities of tasks are derived. Based on dwell-time, the latent recommendation probability of tasks can bepredicted. In addition, trust of tasks is obtained based on theirreputations and participation frequencies. Finally, we performcomprehensive experiments towards the Amazon metadata andYOOCHOOSE data sets to verify the effectiveness of theproposed recommendation model.
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