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Article

A Hybrid Recommender System to Improve Circular Economy in Industrial Symbiotic Networks

1
Knowledge Engineering & Machine Learning Group at Intelligent Data Science and Artificial Intelligence Research Centre ([email protected]), Universitat Politècnica de Catalunya BarcelonaTech (UPC), Catalonia, 08034 Barcelona, Spain
2
Department of Computer Science, Universitat Politècnica de Catalunya BarcelonaTech (UPC), Catalonia, 08034 Barcelona, Spain
3
Department of Statistics and Operations Research, Universitat Politècnica de Catalunya BarcelonaTech (UPC), Catalonia, 08034 Barcelona, Spain
*
Author to whom correspondence should be addressed.
Energies 2019, 12(18), 3546; https://doi.org/10.3390/en12183546
Received: 10 August 2019 / Revised: 4 September 2019 / Accepted: 10 September 2019 / Published: 16 September 2019
Recently, the need of improved resource trading has arisen due to resource limitations and energy optimization problems. Various platforms supporting resource exchange and waste reuse in industrial symbiotic networks are being developed. However, the actors participating in these networks still mainly act based on predefined patterns, without taking the possible alternatives into account, usually due to the difficulty of properly evaluating them. Therefore, incorporating intelligence into the platforms that these networks use, supporting the involved actors to automatically find resources able to cover their needs, is still of high importance both for the companies and the whole ecosystem. In this work, we present a hybrid recommender system to support users in properly identifying the symbiotic relationships that might provide them an improved performance. This recommender combines a graph-based model for resource similarities, while it follows the basic case-based reasoning processes to generate resource recommendations. Several criteria, apart from resource similarity, are taken into account to generate, each time, the list of the most suitable solutions. As highlighted through a use case scenario, the proposed system could play a key role in the emerging industrial symbiotic platforms, as the majority of them still do not incorporate automatic decision support mechanisms. View Full-Text
Keywords: hybrid recommender systems; industrial symbiotic networks; case-based reasoning; waste optimization; energy consumption optimization hybrid recommender systems; industrial symbiotic networks; case-based reasoning; waste optimization; energy consumption optimization
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MDPI and ACS Style

Gatzioura, A.; Sànchez-Marrè, M.; Gibert, K. A Hybrid Recommender System to Improve Circular Economy in Industrial Symbiotic Networks. Energies 2019, 12, 3546. https://doi.org/10.3390/en12183546

AMA Style

Gatzioura A, Sànchez-Marrè M, Gibert K. A Hybrid Recommender System to Improve Circular Economy in Industrial Symbiotic Networks. Energies. 2019; 12(18):3546. https://doi.org/10.3390/en12183546

Chicago/Turabian Style

Gatzioura, Anna; Sànchez-Marrè, Miquel; Gibert, Karina. 2019. "A Hybrid Recommender System to Improve Circular Economy in Industrial Symbiotic Networks" Energies 12, no. 18: 3546. https://doi.org/10.3390/en12183546

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