A task recommendation scheme for crowdsourcing based on expertise estimation

Kurup, Ayswarya R; Sajeev, G P

doi:10.1016/j.elerap.2020.100946

Cited by 15 publications

(5 citation statements)

References 18 publications

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“…Synthetic data generation enables the simulation of stochastic and multi-spectral layouts to enhance ml model testing. This common practice found in the literature [26,27,28,29] supports the generation of relevant scenarios that, in most cases, are absent in the real data. Furthermore, it produces anonymous data, thereby maintaining the privacy of information.…”

Section: Synthetic Data Generationsupporting

confidence: 66%

Simulation, modelling and classification of wiki contributors: Spotting the good, the bad, and the ugly

García-Méndez

Leal

Malheiro

et al. 2022

Simulation Modelling Practice and Theory

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Section: Synthetic Data Generationsupporting

confidence: 66%

Simulation, modelling and classification of wiki contributors: Spotting the good, the bad, and the ugly

García-Méndez

Leal

Malheiro

et al. 2022

Simulation Modelling Practice and Theory

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“…In addition to the above recommendation methods, some other scholars have proposed their recommendation structures by considering worker preferences, historical records, multiparty gains, and workers' reputation. Kurup et al [41] collected workers' preferences and motivational factors from crowdsourcing platform and analyzed that the success rate of bidding is the main factor to affect workers' willingness to participate in the tasks. Therefore, they proposed a task recommendation scheme that considered the participation probability and winning probability of workers.…”

Section: Related Workmentioning

confidence: 99%

A Task Recommendation Model in Mobile Crowdsourcing

Qiu

et al. 2022

Wireless Communications and Mobile Computing

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With the development of the Internet of Things and the popularity of smart terminal devices, mobile crowdsourcing systems are receiving more and more attention. However, the information overload of crowdsourcing platforms makes workers face difficulties in task selection. This paper proposes a task recommendation model based on the prediction of workers’ mobile trajectories. A recurrent neural network is used to obtain the movement pattern of workers and predict the next destination. In addition, an attention mechanism is added to the task recommendation model in order to capture records that are similar to candidate tasks and to obtain task selection preferences. Finally, we conduct experiments on two real datasets, Foursquare and AMT (Amazon Mechanical Turk), to verify the effectiveness of the proposed recommendation model.

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“…Research on crowdsourcing falls into three streams according to the three types of players in crowdsourcing. One deals with platforms, examining innovation coordination mechanisms, task recommendation, quality and risk control, and the design of fraud prevention mechanisms (Hao et al, 2014;Kurup & Sajeev, 2020;Qi et al, 2021;Zhong & Lin, 2015). Another stream focuses on sponsors, exploring enterprise crowdsourcing strategy and influential factors of crowdsourcing performance (Caruana et al, 2006;Mahr et al, 2015;Wang et al, 2014).…”

Section: Crowdsourcing Modelmentioning

confidence: 99%

Verbal vs. Nonverbal Cues in Static and Dynamic Contexts of Fraud Detection in Crowdsourcing

Zhang

Zheng

et al. 2022

Journal of Global Information Management

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As an important mode of open innovation, crowdsourcing can effectively integrate external resources, enabling enterprises to obtain stronger competitiveness and more benefits at a faster speed and lower cost. However, this mode has inevitable intellectual property protection challenges, especially on contest-based crowdsourcing platforms. Previous studies mostly focused on the protection of the rights of sponsors while ignoring the rights of workers, rarely paying attention to sponsor fraud, which may reduce the enthusiasm of participants and eventually turn crowdsourcing;' into a lemon market. This study proposes several fraud detection models to address this problem on contest-based crowdsourcing platforms. Furthermore, this paper explores and compares the value of four types of information as deception cues in crowdsourcing contexts via data mining technology and machine learning methods. The results benefit participants in crowdsourcing markets and contribute to fraud detection research and open innovation in the knowledge economy.

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A task recommendation scheme for crowdsourcing based on expertise estimation

Cited by 15 publications

References 18 publications

Simulation, modelling and classification of wiki contributors: Spotting the good, the bad, and the ugly

Simulation, modelling and classification of wiki contributors: Spotting the good, the bad, and the ugly

A Task Recommendation Model in Mobile Crowdsourcing

Verbal vs. Nonverbal Cues in Static and Dynamic Contexts of Fraud Detection in Crowdsourcing

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