In mobile crowdsensing, the sensing platform recruits users to complete large-scale sensing tasks cooperatively. In order to guarantee the quality of sensing tasks, the platform needs to recommend suitable tasks to users. Existing task recommendation methods typically focus on unilateral factors, such as user preferences or task quality, leading to low platform utility and task acceptance rate respectively. To solve the above issue, this paper proposes a task recommendation method which takes both user preferences and user-task matching into consideration. Firstly, we apply the Deep Interest Network (DIN) in the context of mobile crowdsensing to recommend tasks according to user preferences. Secondly, the concept of user-task matching is introduced, in which both the task difficulty and the user reliability are taken into account. Finally, we propose task recommendation algorithms and conduct extensive experiments on a real dataset. The experimental results show that the proposed method can not only improve the utility of the platform significantly, but also improve the recommendation accuracy slightly under longer recommendation list.