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Register a new userExpFinder: An Ensemble Expert Finding Model Integrating $N$-gram Vector Space Model and $mu$CO-HITS
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Added by
Hung Du
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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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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Expert finding is an important task in both industry and academia. It is challenging to rank candidates with appropriate expertise for various queries. In addition, different types of objects interact with one another, which naturally forms heterogeneous information networks. We study the task of expert finding in heterogeneous bibliographical networks based on two aspects: textual content analysis and authority ranking. Regarding the textual content analysis, we propose a new method for query expansion via locally-trained embedding learning with concept hierarchy as guidance, which is particularly tailored for specific queries with narrow semantic meanings. Compared with global embedding learning, locally-trained embedding learning projects the terms into a latent semantic space constrained on relevant topics, therefore it preserves more precise and subtle information for specific queries. Considering the candidate ranking, the heterogeneous information network structure, while being largely ignored in the previous studies of expert finding, provides additional information. Specifically, different types of interactions among objects play different roles. We propose a ranking algorithm to estimate the authority of objects in the network, treating each strongly-typed edge type individually. To demonstrate the effectiveness of the proposed framework, we apply the proposed method to a large-scale bibliographical dataset with over two million entries and one million researcher candidates. The experiment results show that the proposed framework outperforms existing methods for both general and specific queries.
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