ExpFinder: An Ensemble Expert Finding Model Integrating $N$-gram Vector Space Model and $\mu$CO-HITS

TL;DR

ExpFinder is a new ensemble model that combines an N-gram vector space approach with a graph-based algorithm to improve expert finding accuracy in scientific data, outperforming existing methods significantly.

Contribution

The paper introduces ExpFinder, integrating a novel N-gram vector space model with a modified CO-HITS algorithm for enhanced expert identification.

Findings

ExpFinder outperforms six existing models by 19% to 160.2%.

The N-gram vector space model effectively captures recent inverse document frequency features.

The ensemble approach significantly improves expert finding accuracy.

Abstract

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 $\textit{ExpFinder}$, a new ensemble model for expert finding, that integrates a novel $N$-gram vector space model, denoted as $n$VSM, and a graph-based model, denoted as $\textit{$\mu$CO-HITS}$, that is a proposed variation of the CO-HITS algorithm. The key of $n$VSM is to exploit recent inverse document frequency weighting method for $N$-gram words and $\textit{ExpFinder}$ incorporates $n$VSM into $\textit{$\mu$CO-HITS}$ to achieve expert finding. We comprehensively evaluate $\textit{ExpFinder}$ on four different datasets from the academic domains in comparison with six different expert finding models. The evaluation results show that $\textit{ExpFinder}$ is a highly effective model for expert finding, substantially outperforming all the compared models in 19% to 160.2%.