Sparse　Mobile　Crowdsensing　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-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　reinforcement　learning　(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.　IEEE