Batch Crowdsourcing for Complex Tasks Based on Distributed Team Formation in E-Markets

Jiang, Jiuchuan; Di, Kai; An, Bo; Jiang, Yichuan; Bu, Zhan; Cao, Jie

doi:10.1109/tpds.2022.3161019

Cited by 7 publications

(2 citation statements)

References 38 publications

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“…The authors in [31] introduce the Acceptance-aware Worker Recruitment (AWR) as a novel game in socially aware crowdsourcing, employing a random diffusion model for task invitation propagation on social networks, formulating the AWR game as an NP-hard combinatorial optimization problem to maximize overall task acceptance within a specified incentive budget, and presenting a meta-heuristic-based evolutionary approach, demonstrating its effectiveness and efficiency through comprehensive experiments on real-world datasets. A distributed team formation-based batch crowdsourcing for complex tasks is analyzed in [32], presenting two approaches-forming a fixed team for all tasks or a dynamically adjusted basic team for each task-demonstrating improved cost efficiency, requester payments, communication, task success rates, and scalability compared to previous benchmarks on a real-world dataset. Two crowdworker recruitment strategies are discussed in [33], i.e., platform-based and leader-based, optimizing team formation through an integer linear program considering expertise, social ties, cost, and confidence.…”

Section: Crowdworker Recruitmentmentioning

confidence: 99%

CROWDMATCH: Optimizing Crowdsourcing Matching through the Integration of Matching Theory and Coalition Games

Adesokan,

Kinney,

Tsiropoulou

2024

Future Internet

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This paper tackles the challenges inherent in crowdsourcing dynamics by introducing the CROWDMATCH mechanism. Aimed at enabling crowdworkers to strategically select suitable crowdsourcers while contributing information to crowdsourcing tasks, CROWDMATCH considers incentives, information availability and cost, and the decisions of fellow crowdworkers to model the utility functions for both the crowdworkers and the crowdsourcers. Specifically, the paper presents an initial Approximate CROWDMATCH mechanism grounded in matching theory principles, eliminating externalities from crowdworkers’ decisions and enabling each entity to maximize its utility. Subsequently, the Accurate CROWDMATCH mechanism is introduced, which is initiated by the outcome of the Approximate CROWDMATCH mechanism, and coalition game-theoretic principles are employed to refine the matching process by accounting for externalities. The paper’s contributions include the introduction of the CROWDMATCH system model, the development of both Approximate and Accurate CROWDMATCH mechanisms, and a demonstration of their superior performance through comprehensive simulation results. The mechanisms’ scalability in large-scale crowdsourcing systems and operational advantages are highlighted, distinguishing them from existing methods and highlighting their efficacy in empowering crowdworkers in crowdsourcer selection.

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Section: Crowdworker Recruitmentmentioning

confidence: 99%

CROWDMATCH: Optimizing Crowdsourcing Matching through the Integration of Matching Theory and Coalition Games

Adesokan,

Kinney,

Tsiropoulou

2024

Future Internet

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“…Sociologists use network structure simulations to analyze social interactions [22][23][24][25], with individuals represented as points or nodes and relationships as lines or links [26][27][28]. Since individual, group, societal and national social networks are complex (with relationships involving friendliness, hostility or neutrality), we incorporated two social network definitions: (a) a specific connected network of individuals with a structure that influences social behaviors [29], and (b) a group of social actors (individual or collective) with special ties reflecting different levels of cooperation or opposition [11,[30][31][32].…”

Section: Related Workmentioning

confidence: 99%

Multiagent Diffusion and Opinion Dynamics Model Interaction Effects on Controversial Products

2022

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When controversial products are introduced, effective promotion efforts and eventual public acceptance require consideration of multiple factors such as existing social network structures, numbers of pioneers and their locations, and appropriate methods for product information diffusion. These factors have been the focus of marketing, investment and other studies for many years, most recently in computer information science. Researchers are especially motivated to understand diffusion processes for new technologies and controversial products within and across social networks. While many product diffusion simulation models have been proposed, most suffer from assumptions of unchanging internal agent attitudes toward products, no opinion exchanges between agents, and non-significant relationships between agent internal opinion attitudes and diffusion thresholds. In this paper we propose an opinion dynamics model that assumes both agent interaction and changes in agent attitudes over time. Social psychology theory is used to explain interactions between opinion and diffusion dynamics, with changing agent attitudes and behaviors affected by interpersonal relationship factors. Simulations were used to study dynamic diffusion processes involving controversial products (e.g., vaccines and genetically modified foods) in different social networks. Results indicate that the proposed model accurately reflects several kinds of social phenomena, including pioneer influences, rural marketing strategies, and the influence of social network structure. This effort to identify instances of product diffusion under various social conditions is offered in support of research in communication dynamics and social media-centered marketing strategies.

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Selecting workers like expert for crowdsourcing by integration evaluation of individual and collaborative abilities

Han,

Zhao,

Shan

et al. 2024

Expert Systems with Applications

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Batch Crowdsourcing for Complex Tasks Based on Distributed Team Formation in E-Markets

Cited by 7 publications

References 38 publications

CROWDMATCH: Optimizing Crowdsourcing Matching through the Integration of Matching Theory and Coalition Games

CROWDMATCH: Optimizing Crowdsourcing Matching through the Integration of Matching Theory and Coalition Games

Multiagent Diffusion and Opinion Dynamics Model Interaction Effects on Controversial Products

Selecting workers like expert for crowdsourcing by integration evaluation of individual and collaborative abilities

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