Sparse mobile crowdsensing (MCS) is a cost-effective data collection paradigm that aims to recruit users to collect data from a part of sensing subareas and infer the rest. In a more realistic scenario, users participate in real-time and collect data along the way. For missing data inference, the significance of data collected from different subareas often varies over time. However, since users’ trajectories are uncertain, recruiting users who can cover important spatio-temporal subareas presents a challenge. Additionally, how to segment the budget wisely during recruitment is another challenge. To tackle these challenges, we propose a dual reinforcement learning (RL)-based online user recruitment strategy with adaptive budget segmentation, called DualRL-U, which consists of two alternating decision steps, i.e., the user recruitment decision and the budget retention decision. Specifically, for the user recruitment decision, we use RL to connect the user with data inference accuracy to estimate their contributions. For the budget retention decision, we use RL to connect the budget with the number of times the user can sense to evaluate the cost effectiveness. In this way, a dual RL model is constructed to achieve effective recruitment by alternately executing user recruitment decisions and budget retention decisions. Extensive experiments on real-world sensing data sets show the effectiveness of DualRL-U.