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Mak."],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:p> Crowdsourcing recommendation is necessary to surmount information overload and assist workers to identify tasks of interest efficiently. However, existing studies mainly concentrate on investigating workers\u2019 qualifications or requirements, neglecting their fuzzy expectations and psychological behavior. Besides, workers can only passively accept the results and do not have any right to change them even if they are not satisfactory. As workers will pay different attention to task aspects, we regard the crowdsourcing recommendation as a multi-criteria decision-making (MCDM) problem. In this paper, we propose an MCDM-based method for automated and interactive crowdsourcing task recommendations while comprehensively considering workers\u2019 fuzzy expectations and psychological behavior. Given a large volume of available tasks on a crowdsourcing platform, the requirement of massive human efforts in traditional MCDM is not applicable in such situation. Therefore, we formulate a set of quantitative task evaluation criteria for automated task assessments based on the attributes that have the most impact on workers\u2019 participation behavior. Further, to capture workers\u2019 fuzziness in expectations, an intuitionistic fuzzy 2-tuple linguistic (IF2L) term is introduced, which are further leveraged as reference points to evaluate their \u201cgains\u201d and \u201closses\u201d based on the prospect theory. Finally, the D-S evidence theory is adopted to gather workers\u2019 uncertain prospect values. A case of ZBJ.COM, a popular crowdsourcing platform in China, shows that the proposed method is efficient and effective in recommending suitable and personalized tasks to workers. <\/jats:p>","DOI":"10.1142\/s0219622023500098","type":"journal-article","created":{"date-parts":[[2022,12,20]],"date-time":"2022-12-20T11:43:39Z","timestamp":1671536619000},"page":"413-446","source":"Crossref","is-referenced-by-count":2,"title":["A Novel Crowdsourcing Task Recommendation Method Considering Workers\u2019 Fuzzy Expectations: A Case of ZBJ.COM"],"prefix":"10.1142","volume":"23","author":[{"given":"Biyu","family":"Yang","sequence":"first","affiliation":[{"name":"College of Mechanical and Vehicle Engineering, Chongqing University, 400044, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Longxiao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Business Administration, Chongqing University of Science and Technology, 401331, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Vehicle Engineering, Chongqing University, 400044, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangzhu","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Big Data and Technology, Zhubajie Co. 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