Expert Finding Systems: A Systematic Review

Abstract

The data overload problem and the specific nature of the experts’ knowledge can hinder many users from finding experts with the expertise they required. There are several expert finding systems, which aim to solve the data overload problem and often recommend experts who can fulfil the users’ information needs. This study conducted a Systematic Literature Review on the state-of-the-art expert finding systems and expertise seeking studies published between 2010 and 2019. We used a systematic process to select ninety-six articles, consisting of 57 journals, 34 conference proceedings, three book chapters, and one thesis. This study analyses the domains of expert finding systems, expertise sources, methods, and datasets. It also discusses the differences between expertise retrieval and seeking. Moreover, it identifies the contextual factors that have been combined into expert finding systems. Finally, it identifies five gaps in expert finding systems for future research. This review indicated that ≈65% of expert finding systems are used in the academic domain. This review forms a basis for future expert finding systems research.

Effective distributed representations for academic expert search

“…Voting models We can see documents authored by experts as evidence for their expertise. A partic-ular type of models, based on data fusion methods that aggregate document scores into expert rankings, are called voting models (Husain et al, 2019;Balog et al, 2012).…”

“…Embedding expertise Given the amount of research on document embedding techniques, there has been surprisingly little attention given to the application of contextualized embedding techniques in the field of expertise retrieval. Three recent surveys and reviews on the field of expertise retrieval (Gonçalves and Dorneles, 2019;Husain et al, 2019;Lin et al, 2017) contained little to no information about the application of embedding techniques.…”

“…This process of identifying experts given a particular topic is called expert finding (Balog et al, 2009), expertise retrieval (Gonçalves and Dorneles, 2019), or expert search. Expert search systems are information retrieval systems that can automatically rank candidate experts based on their expertise on a certain subject (Husain et al, 2019). In this study, we target the domain of retrieving academic experts based on papers they authored.…”

“…Given the central role of papers to defining expertise in this domain, we focus on document-centric expert search systems (Balog et al, 2006). These systems largely rely on statistical language modeling, topic modeling, or term frequency-based approaches to represent documents (Gonçalves and Dorneles, 2019;Husain et al, 2019). Surprisingly, given the rapid advances in the field of contextualized text embeddings (Wang et al, 2020b), little work has been done in applying these approaches to document representation for this task.…”

Modeling Academic Research Collaborator Selection Using an Integrated Model

“…Expert finding system helps organizations and individuals to search for suitable experts automatically [19]. Expert finding systems have been used widely to find research collaborators [12,[20][21][22][23].…”