Task recommendation in crowdsourcing systems: A bibliometric analysis

Existing studies on task recommendation in crowdsourcing systems provide additional insights into the field from their perspectives, methodologies, frameworks, and disciplines, resulting in a highly productive but unorganized knowledge domain. This paper is motivated to exploit bibliometric techniques to derive insights that exceed the boundaries of individual systems and identify the potentially transformative changes from 268 published articles. The explicit features (i.e., affiliation, author, citation, and keywords) and implicit information (i.e., topic distribution, potential structure, hidden insights, and evolutionary trend) of domain literature are discovered by network analysis, cluster analysis, and timeline analysis. We summarize the generic framework based on knowledge domain structure and highlight the position of knowledge source, especially textual information, in task recommendation models. Drawing on the Shneider four-stage model, the temporal evolution trend is graphically illustrated to emphasize avenues for future research. Our study conveys accumulated and synthesized specialty knowledge to researchers or newcomers to help them design, initiate, implement, manage, and evaluate recommender systems in crowdsourcing.