Cold-start and data sparsity problems in a digital twin based recommendation system

Pires, Flávia; Moreira, António Paulo; Leitão, Paulo

http://hdl.handle.net/10198/30731

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Authors

Pires, Flávia

Moreira, António Paulo

Leitão, Paulo

Abstract(s)

The emergence of Digital Twins (DT) in Industry 4.0 has enabled the decision support systems taking advantage of more effective recommendation systems (RS). Despite the RS’s growing popularity and ability to support decision-makers, these face two significant challenges, cold-start and data sparsity, which limits the system’s capability to provide effective and accurate decision support. This paper aims to address these issues by conducting a literature review, analysing the current research landscape, and identifying the main enabling methods, algorithms, and similarity measures to mitigate these challenges. The performed analysis enables the point out of future research directions for developing effective and accurate RS that empower decision-makers.

Keywords

Digital Twin Cold-Start Data Sparsity

URI

http://hdl.handle.net/10198/30731

Citation

Pires, Flávia; Moreira, António Paulo; Leitao, Paulo (2024). Cold-start and data sparsity problems in a digital twin based recommendation system. In 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA). Padova: IEEE, p. 1-8. ISBN 979-8-3503-6123-0.