{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T07:33:22Z","timestamp":1770708802196,"version":"3.49.0"},"reference-count":43,"publisher":"Emerald","issue":"8","license":[{"start":{"date-parts":[[2018,4,20]],"date-time":"2018-04-20T00:00:00Z","timestamp":1524182400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["K"],"published-print":{"date-parts":[[2018,9,26]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>Task recommendation is an important way for workers and requesters to get better outcomes in shorter time in crowdsourcing. This paper aims to propose an approach based on 2-tuple fuzzy linguistic method to recommend tasks to the workers who would be capable of completing and accept them.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>In this paper, worker\u2019s capability-to-complete (CTC) and possibility-to-accept (PTA) for a task needs to be recommended are proposed, measured and aggregated to determine worker\u2019s priority for task recommendation. Therein, the similarity between the recommended task and its similar tasks and worker\u2019s performance on these similar tasks are computed and aggregated to determine worker\u2019s CTC quantitatively. In addition, two factors of worker\u2019s active degree and worker\u2019s preferences to a task category are presented to reflect and determine worker\u2019s PTA. In the process of measuring them, 2-tuple fuzzy linguistic method is used to represent, process and aggregate vague and imprecise information.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>To demonstrate the implementation process and performance of the proposed approach, an illustrative example is conducted on Taskcn, a widely used Chinese online crowdsourcing market. The experimental results show that the proposed approach outperformed the self-selection approach, especially for complex or creative tasks. Moreover, comparing with task recommendation considering worker\u2019s CTC solely, the proposed approach would be better in terms of workers\u2019 response rate. Additionally, the use of linguistic terms and fuzzy linguistic method facilitates the expression of vague and subjective information and makes recommendation process more practical.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title>\n<jats:p>In the study, the authors capture alternative workers, collect workers\u2019 behaviors and compute workers\u2019 CTC and PTA manually. However, as the number of tasks and alternative workers grow, the issue, i.e. how to conveniently collect workers\u2019 behaviors and determine their CTC and PTA, becomes conspicuous and needs to be studied further.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title>\n<jats:p>The proposed approach provides an alternative way to perform tasks posted in crowdsourcing platforms. It can assist workers to contribute to right tasks, and requesters to get outcomes with high quality more efficiently.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>This study proposes an approach to task recommendation in crowdsourcing that integrates workers\u2019 CTC and PTA for the recommended tasks and can deal with vague and imprecise information.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/k-12-2017-0468","type":"journal-article","created":{"date-parts":[[2018,4,20]],"date-time":"2018-04-20T09:34:45Z","timestamp":1524216885000},"page":"1623-1641","source":"Crossref","is-referenced-by-count":12,"title":["An approach to task recommendation in crowdsourcing based on 2-tuple fuzzy linguistic 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