Semantic Profiling and Destination Recommendation based on Crowd-sourced Tourist Reviews

Leal, Fátima, González-Vélez, Horacio, Malheiro, Benedita and Burguillo, Juan Carlos (2018) Semantic Profiling and Destination Recommendation based on Crowd-sourced Tourist Reviews. In: Distributed Computing and Artificial Intelligence, 14th International Conference. DCAI 2017. Advances in Intelligent Systems and Computing (620). Springer International Publishing, pp. 140-147. ISBN 978331962410517

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Abstract

Nowadays tourists rely on technology for inspiration, research, booking, experiencing and sharing. Not only it provides access to endless sources of information, but has become an unbounded source of tourist-related data. In such crowd-sourced data-intensive scenario, we argue that new approaches are required to enrich current and new travelling experiences. This work, which supports the “dreaming stage”, proposes the automatic recommendation of personalised destinations based on textual reviews, i.e., a semantic content-based filter of crowd-sourced information. Our approach relies on Topic Modelling – to extract meaningful information from textual reviews – and Semantic Similarity – to identify relevant recommendations. Our main contribution is the processing of crowd-sourced tourism information employing data mining techniques in order to automatically discover untapped destinations on behalf of tourists.

Item Type:	Book Section
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Tourism Industry
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Caoimhe Ní Mhaicín
Date Deposited:	22 Jun 2017 14:44
Last Modified:	24 Jan 2018 09:35
URI:	https://norma.ncirl.ie/id/eprint/2558

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