A News Recommendation System for Environmental Risk Management

Aboutorab, H; Yu, R; Dsouza, A; Saberi, M; Hussain, OK

A News Recommendation System for Environmental Risk Management

Aboutorab, H Yu, R Dsouza, A Saberi, M

Hussain, OK

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Publication Type:: Conference Proceeding
Citation:: CEUR Workshop Proceedings, 2023, 3401
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Aboutorab, H
dc.contributor.author	Yu, R
dc.contributor.author	Dsouza, A
dc.contributor.author	Saberi, M https://orcid.org/0000-0002-5168-2078
dc.contributor.author	Hussain, OK
dc.date.accessioned	2024-03-27T02:07:15Z
dc.date.available	2024-03-27T02:07:15Z
dc.date.issued	2023-01-01
dc.identifier.citation	CEUR Workshop Proceedings, 2023, 3401
dc.identifier.issn	1613-0073
dc.identifier.uri	http://hdl.handle.net/10453/177222
dc.description.abstract	Environmental risk events, such as flooding, can disrupt freight routes and cause business losses. It is therefore crucial to proactively identify and manage these risks. When identifying environmental risks, it is essential to examine the impact of these events on freight routes. In this paper, we extract knowledge about environmental risk events from the knowledge graph and build a machine-learning model to identify freight routes potentially affected by floods. We propose a news recommendation system, namely the News Recommender for Environmental Risk Identification & Analysis (NR-ERIA), to recommend news related to a location of interest that has the risk of being affected by environmental risk events to support the risk management. We conducted experiments on real-world datasets and achieved an accuracy of 0.908 in proactively detecting disruptions, which is 196% higher than the baseline approach, demonstrating the effectiveness of our proposed system.
dc.language	en
dc.relation.ispartof	CEUR Workshop Proceedings
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	4609 Information systems
dc.title	A News Recommendation System for Environmental Risk Management
dc.type	Conference Proceeding
utslib.citation.volume	3401
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2024-03-27T02:07:14Z
pubs.publication-status	Published
pubs.volume	3401

Abstract:

Environmental risk events, such as flooding, can disrupt freight routes and cause business losses. It is therefore crucial to proactively identify and manage these risks. When identifying environmental risks, it is essential to examine the impact of these events on freight routes. In this paper, we extract knowledge about environmental risk events from the knowledge graph and build a machine-learning model to identify freight routes potentially affected by floods. We propose a news recommendation system, namely the News Recommender for Environmental Risk Identification & Analysis (NR-ERIA), to recommend news related to a location of interest that has the risk of being affected by environmental risk events to support the risk management. We conducted experiments on real-world datasets and achieved an accuracy of 0.908 in proactively detecting disruptions, which is 196% higher than the baseline approach, demonstrating the effectiveness of our proposed system.

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http://hdl.handle.net/10453/177222