Hyper-personalized employment in urban hubs: multimodal fusion architectures for personality-based job matching

Tools

Jain, Dipika; Sangwan, Saurabh Raj and Kumar, Akshi. 2024. Hyper-personalized employment in urban hubs: multimodal fusion architectures for personality-based job matching. Neural Computing and Applications, ISSN 0941-0643 [Article] (In Press)

Preview

Text
s00521-024-10587-3.pdf - Published Version
Available under License Creative Commons Attribution.
Download (1MB) | Preview

Official URL: https://link.springer.com/article/10.1007/s00521-0...

Abstract or Description

In the evolving landscape of smart cities, employment strategies have been steering towards a more personalized approach, aiming to enhance job satisfaction and boost economic efficiency. This paper explores an advanced solution by integrating multimodal deep learning to create a hyper-personalized job matching system based on individual personality traits. We employed the First Impressions V2 dataset, a comprehensive collection encompassing various data modalities suitable for extracting personality insights. Among various architectures tested, the fusion of XceptionResNet with BERT emerged as the most promising, delivering unparalleled results. The combined model achieved an accuracy of 92.12%, an R2 score of 54.49%, a mean squared error of 0.0098, and a root mean squared error of 0.0992. These empirical findings demonstrate the effectiveness of the XceptionResNet + BERT in mapping personality traits, paving the way for an innovative, and efficient approach to job matching in urban environments. This work has the potential to revolutionize recruitment strategies in smart cities, ensuring placements that are not only skill-aligned but also personality-congruent, optimizing both individual satisfaction and organizational productivity. A set of theoretical case studies in technology, banking, healthcare, and retail sectors within smart cities illustrate how the model could optimize both individual satisfaction and organizational productivity.

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.1007/s00521-024-10587-3

Data Access Statement:

A publicly available dataset is used in this research; dataset will be made available on request.

Keywords:

Multimodal deep learning, Smart cities, Personality detection, ChaLearn dataset, Urban employment strategies

Departments, Centres and Research Units:

Computing

Dates: