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

• URL: http://arxiv.org/abs/2101.06821v1
• Date: Mon, 18 Jan 2021 00:44:21 GMT
• Title: ExpFinder: An Ensemble Expert Finding Model Integrating $N$-gram Vector Space Model and $\mu$CO-HITS
• Authors: Yong-Bin Kang, Hung Du, Abdur Rahim Mohammad Forkan, Prem Prakash Jayaraman, Amir Aryani, Timos Sellis (Fellow, IEEE)
• Abstract summary: $textitExpFinder$ is a new ensemble model for expert finding. It integrates a novel $N$-gram vector space model, denoted as $n$VSM, and a graph-based model, denoted as $textit$mu$CO-HITS$.
• Score: 0.3560086794419991
• License: http://creativecommons.org/licenses/by/4.0/
• 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%.

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