A unified task recommendation strategy for realistic mobile crowdsourcing system

Li, Zhiyao; Cheng, Bosen; Gao, Xiaofeng; Chen, Huai; Chen, Guihai

doi:10.1016/j.tcs.2020.12.034

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…It is evident that algorithms that require activation functions in their calculations perform best when using the Dice activation function. This function not only solves the problem of gradient explosion during model training, but also significantly alleviates the issue of internal covariate bias [21][22]. The optimal parameters and structures of some algorithms are obtained from the above research results, and these optimal solutions are used in the subsequent calculations.…”

Section: A Analysis Of Training Phases and Optimal Programme Selectionmentioning

confidence: 99%

Design and Implementation of Attention-Based CR System in the Context of Big Data

Wang,

Kuang,

Lyu

2024

IEEE Access

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The recommendation accuracy of traditional product recommendation systems is insufficient. Therefore, an improved deep factor decomposition machine algorithm combining adaptive regularization and attention mechanisms is proposed, and big data components are integrated to enable the algorithm to support more data input types. The publicly available datasets Criteo and Avazu from the Kaggle competition were selected for testing experiments in the study. The experimental results are as follows. During the training phase, after the convergence of each model, the loss function value of the model designed in this study is 1.26, which is the lowest among all comparison models. Moreover, when the number of recommended products is 7, the overall recommendation effect of each model is the best. The area under the curve of the subject operation characteristic curve of the model designed in this study on the Criteo dataset is 0.809, which is significantly higher than all comparison models. It is believed that this model has higher recommendation accuracy and can be used in application scenarios that require higher recommendation quality.

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Section: A Analysis Of Training Phases and Optimal Programme Selectionmentioning

confidence: 99%

Design and Implementation of Attention-Based CR System in the Context of Big Data

Wang,

Kuang,

Lyu

2024

IEEE Access

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“…The effectiveness of the proposed recommendation mechanism is evaluated by analyzing the participants' feedback. Li et al [32] formally defined the Accurate Recommendation Problem (ARP) and proved the problem was NP-Hard. To address this problem, they designed a task recommendation system, Pioneer-Assisted Task Recommendation (PATRON) framework.…”

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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Comprehensive Review of Learnable and Adaptive Recommendation Systems

Dongre

Agrawal

2022

Emerging Technologies in Data Mining and Information Security

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A unified task recommendation strategy for realistic mobile crowdsourcing system

Cited by 6 publications

References 10 publications

Design and Implementation of Attention-Based CR System in the Context of Big Data

Design and Implementation of Attention-Based CR System in the Context of Big Data

A Task Recommendation Model in Mobile Crowdsourcing

Comprehensive Review of Learnable and Adaptive Recommendation Systems

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