SMART Restaurant ReCommender: A Context-Aware Restaurant Recommendation Engine

Ubaid, A; Lie, A; Lin, X

SMART Restaurant ReCommender: A Context-Aware Restaurant Recommendation Engine

Ubaid, A

Lie, A Lin, X

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: AI, 2025, 6, (4)
Issue Date:: 2025-03-25

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Field	Value	Language
dc.contributor.author	Ubaid, A https://orcid.org/0000-0002-9833-9891
dc.contributor.author	Lie, A
dc.contributor.author	Lin, X https://orcid.org/0000-0002-0133-4737
dc.date.accessioned	2025-05-13T00:58:37Z
dc.date.available	2025-05-13T00:58:37Z
dc.date.issued	2025-03-25
dc.identifier.citation	AI, 2025, 6, (4)
dc.identifier.issn	2673-2688
dc.identifier.issn	2673-2688
dc.identifier.uri	http://hdl.handle.net/10453/187332
dc.description.abstract	<jats:p>With the rise of e-commerce and web application usage, recommendation systems have become important to our daily tasks. They provide personalized suggestions to assist with any task under consideration. While various machine learning algorithms have been developed for recommendation tasks, existing systems still face limitations. This research focuses on advancing context-aware recommendation sytems by leveraging the capabilities of Large Language Models (LLMs) in conjunction with real-time data. The research exploits the integration of existing real-time data APIs with LLMs to enhance the capabilities of the recommendation systems already integrated into smart societies. The experimental results demonstrate that the hybrid approach significantly improves the user experience and recommendation quality, ensuring more relevant and dynamic suggestions.</jats:p>
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	AI
dc.relation.isbasedon	10.3390/ai6040064
dc.rights	info:eu-repo/semantics/openAccess
dc.title	SMART Restaurant ReCommender: A Context-Aware Restaurant Recommendation Engine
dc.type	Journal Article
utslib.citation.volume	6
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.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-05-13T00:58:34Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	6
utslib.citation.issue	4

Abstract:

With the rise of e-commerce and web application usage, recommendation systems have become important to our daily tasks. They provide personalized suggestions to assist with any task under consideration. While various machine learning algorithms have been developed for recommendation tasks, existing systems still face limitations. This research focuses on advancing context-aware recommendation sytems by leveraging the capabilities of Large Language Models (LLMs) in conjunction with real-time data. The research exploits the integration of existing real-time data APIs with LLMs to enhance the capabilities of the recommendation systems already integrated into smart societies. The experimental results demonstrate that the hybrid approach significantly improves the user experience and recommendation quality, ensuring more relevant and dynamic suggestions.

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http://hdl.handle.net/10453/187332