1 research outputs found
ExpFinder: An Ensemble Expert Finding Model Integrating -gram Vector Space Model and CO-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 , a new
ensemble model for expert finding, that integrates a novel -gram vector
space model, denoted as VSM, and a graph-based model, denoted as
\textit{\muCO-HITS}, that is a proposed variation of the CO-HITS algorithm.
The key of VSM is to exploit recent inverse document frequency weighting
method for -gram words and incorporates VSM into
\textit{\muCO-HITS} to achieve expert finding. We comprehensively evaluate
on four different datasets from the academic domains in
comparison with six different expert finding models. The evaluation results
show that is a highly effective model for expert finding,
substantially outperforming all the compared models in 19% to 160.2%.Comment: 15 pages, 18 figures, "for source code on Github, see
https://github.com/Yongbinkang/ExpFinder", "Submitted to IEEE Transactions on
Knowledge and Data Engineering