Task recommendation in crowdsourcing systems: A bibliometric analysis

Xicheng Yin, Hongwei Wang, Wei Wang and Kevin Zhu

Technology in Society, 2020, vol. 63, issue C

Abstract: 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.

Keywords: Task recommendation; Task assignment; Crowdsourcing; Bibliometrics; Recommender system (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0160791X20302049
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:teinso:v:63:y:2020:i:c:s0160791x20302049

DOI: 10.1016/j.techsoc.2020.101337

Access Statistics for this article

Technology in Society is currently edited by Charla Griffy-Brown

More articles in Technology in Society from Elsevier
Bibliographic data for series maintained by Catherine Liu ().