More Web Proxy on the site http://driver.im/

research-article

An Optimal Travel Route Recommender System for Tourists in Om Non Canal, Thailand

Author:

Pijitra JomsriAuthors Info & Claims

ISCSIC 2019: Proceedings of the 2019 3rd International Symposium on Computer Science and Intelligent Control

Article No.: 36, Pages 1 - 5

https://doi.org/10.1145/3386164.3389103

Published: 06 June 2020 Publication History

Abstract

The unique tourism style of Thailand is boat trip. Om Non Canal is the canal that still has the original waterway lifestyle. There are many tourist attractions such as cultural attractions, floating market routes, and Thai way of tourist attractions. Therefore, in this research, Machine Learning Based Approach Techniques and Analytic Hierarchy Process Techniques is applied for introducing the attractions by considering POIs (Points of Interest), travel dates, previous attractions which users travel to support the development and to introduce the information of water travel attractions around the Om Non Canal. From the results of the experiment, it was found that the travel route recommender system is suitable for tourism planning around the Om Non Canal. It is useful for the tourists and the tourism business operators.

References

[1]

A. Luberg, T. Tammet and P. Järv, (2011). Smart city: A rulebased tourist recommendation system. In R. Law, M. Fuchs, & F. Ricci (Eds.), Information and communication technologies in tourism 2011.

[2]

A. Mohd, J. Hosking and J. Grundy (2010). A taxonomy of computer-supported critics," Information Technology (ITSim), International Symposium, Vol.3.

[3]

C. Cheng, H. Yang, M.R.L yu, I. King (2013). Where you like to go next: Successive point-of-interest recommendation. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, Beijing, China, 3--9 August 2013, 2605--2611.

[4]

C. Wannathanom (2016). Comments role of the guide Thailand travel guide for tourists to raise awareness and consciousness. International journal of systems applications, engineering & development, 10, 31--34.

[5]

H. Huang, G. Gartner, (2014). Using trajectories for collaborative filtering--based POI recommendation. Int. J. Data Min. Model. Manag., 6, 333--346.

[6]

I. Mínguez, D. Berrueta and L. Polo (2010). CRUZAR: An application of semantic matchmaking to e-tourism. In Y. Kalfoglou (Ed.), Cases on semantic interoperability for information systems integration: Practices and application, 255--271.

[7]

J. Bao, Y. Wilkie, D. Mokbel M. (2015). Recommendations in location-based social networks: A survey. Geoinformatica 19, 525--565.

Digital Library

[8]

L. Sebastià, I. Garcia, E. Onaindia and C. Guzman (2009). Etourism: A tourist recommendation and planning application. International Journal on Artificial Intelligence Tools, 18(5), 717--738.

[9]

M. Clements, P. Serdyukov, A. de Vries, M. Reinders (2011) Personalised travel recommendation based on location cooccurrence. arXiv.

[10]

Miniwatts Marketing Group. (2012). Internet world stats, http://www.internetworldstats.com.

[11]

Nonthaburi Provincial Cultural Office (2017). https://www.tourismthailand.org/About-Thailand/Destination/Nonthaburi.

[12]

P. Jomsri, (2014). Book Recommendation system for digital library based on user profiles by using association rule. Fourth edition of the International Conference on the Innovative Computing Technology (INTECH 2014).

[13]

P. Jomsri, D.Prangchumpol, (2015). A hybrid model ranking search result for research paper searching on social bookmarking", 1st International Conference on Industrial Networks and Intelligent Systems (INISCom), IEEE, pp. 38--43.

[14]

P. Vansteenwegen, W. Souffriau, B. Vanden and D. Van Oudheusden (2010). The city trip planner: An expert system for tourists. Expert Systems with Applications, 38(6), 6540--6546.

Digital Library

[15]

S. Zhao, I. King, M.R. Lyu, (2015). A Survey of Point-of-interest Recommendation in Location-based Social Networks. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, Austin, TX, USA, 25--30 January 2015.

[16]

T. Kurashima, T. Iwata, G. Irie, K. Fujimura, (2010). Travel route recommendation using geotags in photo sharing sites. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management, Toronto, ON, Canada, 26--30 October 2010, 579--588.

Digital Library

[17]

W. Souffriau and P. Vansteenwegen, (2010). Tourist Trip Planning Functionalities: State--of--the--Art and Future Current Trends in Web Engineering. vol. 6385, 474--485.

[18]

Y. Shi, P. Serdyukov, A. Hanjalic and M. Larson, (2011). Personalized landmark recommendation based on geo-tags from photo sharing sites. In Proceedings of the 5th AAAI Conference Weblogs Social Media, Barcelona, Spain, 17--21 July 2011; Volume 11, 622--625.

Cited By

Aldayel MAl-Nafjan AAl-Nuwaiser WAlrehaili GAlyahya G(2023)Collaborative Filtering-Based Recommendation Systems for Touristic Businesses, Attractions, and DestinationsElectronics10.3390/electronics1219404712:19(4047)Online publication date: 27-Sep-2023
https://doi.org/10.3390/electronics12194047
Jomsri P(2020)Usability Evaluation for User Interface Design of Application for Recommender System to Enhance the Potential of Community-Based Tourism in Phatthalung, ThailandJournal of Physics: Conference Series10.1088/1742-6596/1627/1/0120091627(012009)Online publication date: 4-Sep-2020
https://doi.org/10.1088/1742-6596/1627/1/012009

Index Terms

An Optimal Travel Route Recommender System for Tourists in Om Non Canal, Thailand
1. Information systems
  1. Information retrieval
    1. Users and interactive retrieval
  2. World Wide Web
    1. Web searching and information discovery
      1. Personalization

Recommendations

Scenic route planning for tourists

Tourists visiting unknown destinations become increasingly dependent on mobile city guides to locate tourist services and retrieve informative content about nearby points of interest (POIs). Several mobile guides already support the provision of ...
An integrated recommender system for multi-day tourist itinerary
Abstract
Planning a personalized itinerary that satisfies the preferences and limitations of a tourist can be a complex task. The challenge is further compounded by the lack of information about tourists' tastes, which makes it difficult to ensure their ...
Highlights
- Proposes integrated recommender system for multi-day tourist itineraries.
- Uses hybrid prediction method based on interests, categorization, and similarity.
- Formulates problem as multi-day orienteering with count-dependent profits.
Modeling Tourists' Personality in Recommender Systems: How Does Personality Influence Preferences for Tourist Attractions?
UMAP '20: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization

Personalization is increasingly being perceived as an important factor for the effectiveness of Recommender Systems (RS). This is especially true in the tourism domain, where travelling comprises emotionally charged experiences, and therefore, the more ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

ISCSIC 2019: Proceedings of the 2019 3rd International Symposium on Computer Science and Intelligent Control

September 2019

397 pages

ISBN:9781450376617

DOI:10.1145/3386164

Copyright © 2019 ACM.

© 2019 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 June 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ISCSIC 2019

ISCSIC 2019: 2019 3rd International Symposium on Computer Science and Intelligent Control

September 25 - 27, 2019

Amsterdam, Netherlands

Acceptance Rates

ISCSIC 2019 Paper Acceptance Rate 77 of 152 submissions, 51%;

Overall Acceptance Rate 192 of 401 submissions, 48%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
77
Total Downloads

Downloads (Last 12 months)11
Downloads (Last 6 weeks)0

Reflects downloads up to 25 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Aldayel MAl-Nafjan AAl-Nuwaiser WAlrehaili GAlyahya G(2023)Collaborative Filtering-Based Recommendation Systems for Touristic Businesses, Attractions, and DestinationsElectronics10.3390/electronics1219404712:19(4047)Online publication date: 27-Sep-2023
https://doi.org/10.3390/electronics12194047
Jomsri P(2020)Usability Evaluation for User Interface Design of Application for Recommender System to Enhance the Potential of Community-Based Tourism in Phatthalung, ThailandJournal of Physics: Conference Series10.1088/1742-6596/1627/1/0120091627(012009)Online publication date: 4-Sep-2020
https://doi.org/10.1088/1742-6596/1627/1/012009

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten