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research-article

Tourism recommendation system: a survey and future research directions

Authors:

Joy Lal Sarkar,

Abhishek Majumder,

Chhabi Rani Panigrahi,

Bibudhendu PatiAuthors Info & Claims

Multimedia Tools and Applications, Volume 82, Issue 6

Pages 8983 - 9027

https://doi.org/10.1007/s11042-022-12167-w

Published: 21 April 2022 Publication History

Abstract

A Recommendation System (RS) is an intelligent computer based system which provide valuable suggestions to the user and are used in several domains. Social media platforms are the most common internet applications due to the large number of users. The numerous posts, likes, etc. have accrued on social media platforms and can be used in variety of recommendation systems. In this work, the primary focus is the tourism domain, where RS serves as a valuable tool for the tourist to plan his trip. Traditional RS systems only cater to the needs of the tourist by examining few factors. However, there are a large range of factors such as environment factors, actual geo-coordinates, trip destination, preferences of the user etc. that need to be taken into account in order to make a foolproof recommendation to the tourists. Tourism Recommendation Systems (TRS) provide suggestions to the tourists to identify the most suited transport (flight, train, etc.), accommodations, museums, special interest places and other items which are required for the trip. Several techniques are used and a thorough study of various techniques of traditional RS and TRS techniques have been done which are specially designed for tourism domain. Various Artificial Intelligence (AI) techniques have been highlighted which are used to solve the tourist recommendation problem. Also, future research direction has been suggested which would improvise the Quality of Service (QoS) of the RS in tourism industry.

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Cited By

Zhou WGuo FXu HWang Z(2024)Joint friend and item recommendation based on multidimensional feature reciprocal interaction in social e-commerceElectronic Commerce Research and Applications10.1016/j.elerap.2024.10140665:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.elerap.2024.101406
Orabi MAfyouni IAl Aghbari Z(2023)Harnessing Extracted Social Media Events for Personalized Travel SequencesProceedings of the 7th ACM SIGSPATIAL Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising10.1145/3615896.3628346(44-49)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3615896.3628346

Index Terms

Tourism recommendation system: a survey and future research directions

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

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Published In

cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 82, Issue 6

Mar 2023

1544 pages

ISSN:1380-7501

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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.

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Kluwer Academic Publishers

United States

Publication History

Published: 21 April 2022

Accepted: 07 January 2022

Revision received: 11 November 2021

Received: 06 November 2020

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Cited By

Zhou WGuo FXu HWang Z(2024)Joint friend and item recommendation based on multidimensional feature reciprocal interaction in social e-commerceElectronic Commerce Research and Applications10.1016/j.elerap.2024.10140665:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.elerap.2024.101406
Orabi MAfyouni IAl Aghbari Z(2023)Harnessing Extracted Social Media Events for Personalized Travel SequencesProceedings of the 7th ACM SIGSPATIAL Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising10.1145/3615896.3628346(44-49)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3615896.3628346

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