Worker recruitment in mobile crowdsensing systems aims to recruit the most suitable users to perform tasks with high quality and in real-time. Many worker recruitment or task matching mechanisms have been proposed, especially for crowdsourcing platforms, where content information of tasks from the implicit feedback of workers' attendance is extensively exploited to help workers find preferred tasks efficiently. Different from traditional crowdsourcing systems, tasks in mobile crowdsensing systems are usually time-sensitive and location-dependent which also play a crucial role in worker recruitment. However, these context information have not been effectively explored for user recruitment in mobile crowdsensing systems. In this paper, we propose a novel personalized task-oriented worker recruitment mechanism for mobile crowdsensing systems based on a careful characterization of workers' preference. In particular, we fully exploit the content information (e.g., task category, task description) together with the context information (e.g., task time, task location) from the implicit feedback of workers' attendance to accurately model workers' preference on tasks. Moreover, we regard the task-worker fitness prediction as a binary classification problem and utilize the Logit model to integrate the heterogeneous factors into a single framework to predict the matching probability of each task-worker pair. Finally, the workers with the highest matching probability are recruited proactively for each new task. Extensive experiments on real-world datasets demonstrate that the proposed mechanism achieves better performance than the benchmarks.