Expertise retrieval has been the subject of intense research over the past decade, particularly with the public availability of benchmark test collections for expertise retrieval in enterprises. Another domain which has seen comparatively less research on expertise retrieval is academic search. In this paper, we describe the Lattes Expertise Retrieval (LExR) test collection for research on academic expertise retrieval. LExR has been designed to provide a large-scale benchmark for two complementary expertise retrieval tasks, namely, expert profiling and expert finding. Unlike currently available test collections, which fully support only one of these tasks, LExR provides graded relevance judgments performed by expert judges separately for each task. In addition, LExR is both cross-organization and cross-area, encompassing candidate experts from all areas of knowledge working in research institutions all over Brazil. As a result, it constitutes a valuable resource for fostering new research directions on expertise retrieval in an academic setting.

Building expertise profiles is a crucial step towards identifying experts in different knowledge areas. However, summarizing 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 expertise 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 unsupervised tag recommendation approaches as well as multiple sources of textual evidence. Our analysis reveals that traditional content-based tag recommenders perform well at identifying 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.