Building expertise profiles is a crucial step towards iden-tifying experts in different knowledge areas. However, sum-marizing the topics of expertise of a given individual is a challenging task, primarily due to the semi-structured and heterogeneous nature of the documentary evidence available for this task. In this paper, we investigate the suitability of tag recommendation as a mechanism to produce effective ex-pertise profiles. In particular, we perform a large-scale user study with academic experts from different knowledge areas to assess the effectiveness of multiple supervised and unsu-pervised tag recommendation approaches as well as multiple sources of textual evidence. Our analysis reveals that tradi-tional content-based tag recommenders perform well at iden-tifying expertise-oriented tags, with article keywords being a particularly effective source of evidence across profiles in different knowledge areas and with various levels of sparsity. Moreover, by combining multiple recommenders and sources of evidence as learning signals, we further demonstrate the effectiveness of tag recommendation for expertise profiling.