ExpFinder: An Ensemble Expert Finding Model Integrating N-gram Vector Space Model and μCO-HITS

Finding an expert plays a crucial role in driving successful collaborations and speeding up high-quality research development and innovations. However, the rapid growth of scientific publications and digital expertise data makes identifying the right experts a challenging problem. Existing approaches for finding experts given a topic can be categorised into information retrieval techniques based on vector space models, document language models, and graph-based models. In this paper, we propose textitExpFinder, a new ensemble model for expert finding, that integrates a novel N-gram vector space model, denoted as nVSM, and a graph-based model, denoted as textitμCO-HITS, that is a proposed variation of the CO-HITS algorithm. The key of nVSM is to exploit recent inverse document frequency weighting method for N-gram words and textitExpFinder incorporates nVSM into textitμCO-HITS to achieve expert finding. We comprehensively evaluate textitExpFinder on four different datasets from the academic domains in comparison with six different expert finding models. The evaluation results show that textitExpFinder is a highly effective model for expert finding, substantially outperforming all the compared models in 19% to 160.2%.