基于时空信息和任务流行度分析的移动群智感知任务推荐

现有移动群智感知任务推荐的共同缺点是：一方面，未充分考虑时空信息对工人偏好的影响，导致推荐准确性低；另一方面，忽略了任务流行度对推荐的影响，导致推荐覆盖率差。为解决这些问题，提出一种基于时空信息和任务流行度分析的移动群智感知任务推荐方法。充分利用工人执行记录中的相关信息（如工人执行任务的时间、位置），准确预测工人对任务的偏好；基于工人声誉和任务执行情况分析任务流行度并设计任务流行度惩罚因子，提升推荐效果的覆盖率；结合工人偏好和流行度惩罚因子生成任务推荐列表。实验结果表明，与现有基线方法相比，所提出方法在推荐准确率上平均提升了3.5%，推荐覆盖率上平均提高了25%。

Task recommendation based on spatial-temporal information and task popularity analysis in mobile crowd sensing

The drawbacks of existing task recommendation in mobile crowd sensing were as follows: on the one hand, not fully considering the influence of spatial-temporal information on worker preference led to low accuracy of recommendation, on the other hand, ignoring the impact of task popularity on recommendation led to poor recommendation coverage. To solve these drawbacks, this paper proposed a novel task recommendation approach based on spatial-temporal information and task popularity analysis in mobile crowd sensing. Firstly, this approach made full use of the relevant information contained in the worker execution record(e. g., the time and location of worker performing tasks) to accurately predict the preference of worker for performing tasks. Secondly, in order to reduce the impact of popular tasks on recommendation coverage, this paper analyzed task popularity based on worker reputation and task execution record, and designed appropriate task popularity penalty factor. Then, combining worker preference and task popularity penalty factor, this paper provided an appropriate task recommendation list for each worker. Finally, the experimental results show that compared with the existing baseline methods, the proposed method improves the recommendation accuracy by 3.5% and the recommendation coverage by 25%.