Incorporating worker rivalry into task recommendations on crowdsourcing platforms: A novel framework for boosting participation and efficiency

IF 7.4 1区 管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Hefu Liu , Wenlong Li , Meng Chen , Juntao Wu
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引用次数: 0

Abstract

Crowdsourcing platforms have demonstrated significant advantages in addressing complex business and societal challenges. However, their task recommendation systems face dual challenges stemming from information overload and inadequate modeling of competitive relationships. While existing studies primarily utilize collaborative filtering and content-based approaches for task recommendation, these methods typically overlook the systematic impact of dynamic rivalry among workers in open crowdsourcing environments. To address this gap, we propose the Crowdsourcing Task Recommendation model with Competitive Relationships among Workers (CTRCRW), integrating a three-dimensional rivalry modeling mechanism (rivalry-similarity, repeated competition, and evenly matched competition) with deep learning techniques. Specifically, CTRCRW develops a multi-dimensional rivalry quantification approach and introduces a Rivalry Attention Module (RAM), leveraging graph neural networks combined with cosine similarity weights and a learnable gating mechanism to capture explicit competitive behaviors and implicit psychological motivations. Experiments on real-world datasets confirm that CTRCRW significantly improves recommendation accuracy and competitive rationality, effectively reducing workers’ search costs. This study contributes to theory and methodology for relationship-driven recommendations in crowdsourcing, providing generalized insights for resource allocation in complex interactive environments.
将工人竞争纳入众包平台的任务建议:提高参与和效率的新框架
众包平台在解决复杂的商业和社会挑战方面已经显示出显著的优势。然而,他们的任务推荐系统面临着来自信息过载和竞争关系建模不足的双重挑战。虽然现有的研究主要利用协作过滤和基于内容的方法进行任务推荐,但这些方法通常忽略了开放众包环境中工人之间动态竞争的系统影响。为了解决这一差距,我们提出了具有工人之间竞争关系的众包任务推荐模型(CTRCRW),将三维竞争建模机制(竞争相似,重复竞争和均匀匹配竞争)与深度学习技术相结合。具体而言,CTRCRW开发了一种多维竞争量化方法,并引入了竞争注意模块(RAM),利用结合余弦相似度权重的图神经网络和可学习的门控机制来捕捉外显竞争行为和内隐心理动机。在真实数据集上的实验证实,CTRCRW显著提高了推荐的准确性和竞争合理性,有效降低了工人的搜索成本。本研究为众包中关系驱动的推荐提供了理论和方法,为复杂交互环境中的资源分配提供了一般性见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Information Processing & Management
Information Processing & Management 工程技术-计算机:信息系统
CiteScore
17.00
自引率
11.60%
发文量
276
审稿时长
39 days
期刊介绍: Information Processing and Management is dedicated to publishing cutting-edge original research at the convergence of computing and information science. Our scope encompasses theory, methods, and applications across various domains, including advertising, business, health, information science, information technology marketing, and social computing. We aim to cater to the interests of both primary researchers and practitioners by offering an effective platform for the timely dissemination of advanced and topical issues in this interdisciplinary field. The journal places particular emphasis on original research articles, research survey articles, research method articles, and articles addressing critical applications of research. Join us in advancing knowledge and innovation at the intersection of computing and information science.
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