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• W3201433306 abstract "The worker-task matching problem brings up unique characteristics that are not present in traditional matching scenarios, i.e., the huge flow of tasks with short lifespans, the importance of workers’ capabilities, and the quality of the completed tasks. These characteristics further pose significant challenges of data sparsity and comprehensive modeling.To address the two challenges, this paper proposes MvkGAN, a multi-view attention network on a bi-collaborative knowledge graph (BicKG). The core ideas of our work are 1) building BicKG from the data of workers, tasks, their interactions, and domain knowledge, and then leveraging it to reveal the latent interactions between workers and tasks to mitigate the data sparsity challenge; and 2) designing a multi-view knowledge graph attention network (MvkGAN) which learns to match workers and tasks, to meet the comprehensive modeling challenge. In this network, different features are organized as multiple views and these views are further connected by the attention mechanism.We have implemented MvkGAN and evaluated it against five state-of-the-art approaches (Wide&Deep, DeepFM, KGAT, Crow-dRex and PJFNN) on two real-world datasets. The evaluation results show that MvkGAN improves the accuracy by 6.90% and F1-score by 5.52% on average, and also has the ability of generating reasonable explanations." @default.
• W3201433306 created "2021-09-27" @default.
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• W3201433306 date "2021-07-01" @default.
• W3201433306 modified "2023-09-26" @default.
• W3201433306 title "Learning to Match Workers and Tasks via a Multi-View Graph Attention Network" @default.
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• W3201433306 doi "https://doi.org/10.1109/compsac51774.2021.00035" @default.
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