As an emerging online interaction service in Twitter-like social media systems, mention serves to significantly improve both user interaction experience and information propagation. In recent years, the problem of mentionee recommendation, i.e., recommending mentionees (mentioned users) when mentioners (mentioning users) mention others, has received considerable attention. However, the extreme sparsity of mentioner-mentionee matrix creates a severe challenge. While an increasing line of work has exploited diverse effects such as the textual content and spatio-temporal context influences to cope with this challenge, there lacks a comprehensive study of the joint effect of all these influencing factors. In light of this, we propose a joint latent-class probabilistic model, named Joint Topic-Area Model (JTAM), to tackle the mentionee recommendation problem by simultaneously learning and modeling users’ semantic interests, the spatio-temporal mentioning patterns of mentioners, the geographical distribution of mentionees, and their joint effects on users’ mention behaviors in a unified way. Moreover, to facilitate online query performance, we design an efficient query answering approach that enables fast top-k mentionee recommendation. To evaluate the performance of our method, we conduct extensive experiments on a large real-world dataset. The results demonstrate the superiority of our method in recommending mentionees in terms of both effectiveness and efficiency compared with other state-of-the-art methods.