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Cognition2Vocation: meta-learning via ConvNets and continuous transformers

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Abstract

Estimating the suitability of individuals for a vocation via leveraging the knowledge within cognitive factors comes with numerous applications: employment resourcing, occupation counseling, and workload management. Accordingly, the enterprises aim to hire the most suitable person from a massive array of similar applicants, maximizing performance and minimizing the gap between strategic indicators and predefined targets. While cognitive factors signify the best-suited person from similarly skilled workers, inferring pertinent latent cues from noisy and growing social network contents is time-intensive. To tackle the challenges involved, we propose a framework that, on the one hand, extends influential features based on the correlations between cognitive cues and, on the other hand, leverages a novel continuous transformer to mitigate the overlapping and approximation issues in discrete modeling. Rather than relying on discrete patterns that may evolve frequently, we use continuous elements that include not only numerous aggregating components but also sense minor irregular fluctuations. In a hybrid manner, we fuse multiple base models to transfer a higher representation to the meta-learning unit, agglomerating outputs from gradient boosters and the ConvNets. The experimental results show that our proposed framework can outperform trending vocation estimation methods by 1.36% in F1-Score and approximately 1% in accuracy.

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Data availability

The dataset is available at https://sites.google.com/view/cognition-computing-lab.

Notes

  1. https://sites.google.com/view/cognition-computing-lab

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Authors and Affiliations

  1. School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran

    Sara Kamran, Sayna Esmailzadeh & Mohammad Reza Kangavari

  2. Faculty of Computing and Information Technology, Sohar University, Sohar, Oman

    Saeid Hosseini

  3. Department of Computing, The Hong Kong Polytechnic University, Hong Kong, Hong Kong

    Wen Hua

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  1. Sara Kamran
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  2. Saeid Hosseini
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  4. Mohammad Reza Kangavari
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  5. Wen Hua
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Correspondence to Saeid Hosseini.

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Kamran, S., Hosseini, S., Esmailzadeh, S. et al. Cognition2Vocation: meta-learning via ConvNets and continuous transformers. Neural Comput & Applic 36, 12935–12950 (2024). https://doi.org/10.1007/s00521-024-09749-0

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