Several challenging issues have yet to be jointly addressed in the recommendation of experts for community question answering, including dynamicity, comprehensive profiling, the incorporation of auxiliary data, and the manipulation of heterogeneous information. We argue that a unified treatment of these issues is beneficial in improving recommendation effectiveness. In this paper, we introduce and formalize a new and more thorough instance of the expert-recommendation task for community question answering, which is conceived to suitably account for the connections among the targeted issues. Moreover, in order to carry out the devised task, we present an innovative Bayesian tag-based approach that systematically handles all of the targeted issues in a coherent and seamlessly unified manner. At the heart of the proposed approach is an unprecedented principled fusion of various types of information. The integrated information enables a peculiar characterization of community members in terms of three inherent properties, i.e., their tag-based temporally-discounted interest, expertise, and willingness to respond. The first property is determined by looking into questions, while the last two are determined from answers. Within a generic question answering community, the three properties of its members allow for selectively routing any question to a specifically addressed set of responders. These are recommended as trustworthy repliers, who are not only actually experts in the themes of the routed question as per its tags, but also still interested in such themes and willing to answer at routing time. An extensive empirical assessment involving real-world benchmark data from heterogeneous domains reveals the higher recommendation effectiveness of the presented approach compared to state-of-the-art competitors. • A new task is devised to jointly treat open issues in expert recommendation for CQA. • Dynamicity, comprehensive profiling, auxiliary data, heterogeneous information. • The above task is carried out via an innovative tag-based time-sensitive approach. • Users’ interest, expertise and willingness to reply modeled via information fusion. • Any question is routed to a specifically recommended set of trustworthy repliers.