As a newly emerging social media user interactive service, mention mechanism is playing an important role in both information sharing and online social interacting. Researches on mention mechanism can provide us valuable resources to reveal the correlation between users' latent preferences and their explicit interacting behaviors and can be constructed as the data foundation for many applications such as information dissemination monitoring, business intelligence, and personalized recommendation. However, most of the previous works focused on the information diffusion aspect, lacking the in-depth study on its interaction attribute from the common users' perspective. This study aims to construct a recommendation system to automatically recommend target users for given social media posts based on the analysis and modeling of common users' mention behaviors. This study first analyzes two large-scale real-world datasets to explore the mention mechanism from the aspect of users' interactions and finds that, users' mention behaviors are impacted by both the semantic and the spatial context of their mention activities. Secondly, based on a unified definition of the joint semantic and spatial context-aware mention behavior, a joint latent probabilistic generative model named JUMBM (joint user mention behavior model) is built to simulate the generating process of users' mention activities. Specially, JUMBM is able to simultaneously capture users' movement patterns, geographical area-dependent semantic interests, and the geographical clustering patterns of the targets users. Besides, a hybrid pruning algorithm is proposed to achieve a fast high-dimensional retrieval and facilitate the online top-k query answering. Extensive experiments on real-world datasets demonstrate the significant superiority of the proposed approach over the baseline methods to make more effective and efficient recommendations.