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Travelopia: A Tourism Recommendation System


Affiliations
1 Department of CSE, Global Academy of Technology, Bengaluru, India
2 Department of CSE, Global Academy of Technology,Bengaluru, India
3 HOD, Department of CSE, Global Academy of Technology, Bengaluru, India
     

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Recommendation Systems are one of the most appropriate applications of machine learning. They are the cluster of simple algorithms that provide the most significant and precise data as per the user's need. The Tourism domain is one of the predominant economic areas of a nation and the second-largest foreign exchange earner for India. They find relationships between users based on their actions, without any human curation involved at all. Travel Recommendation System (TRS) is a recommendation system used by tourists and travelers to fulfill their needs and make decisions about travel destinations, Points of Interest, restaurants, etc. The System Helps in making the tourism industry a "Smart Tourism Industry".

Keywords

Recommendation System, Filtering Approach, Association Rule Learning, Feature Selection.
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  • SupriyaRajebhosale, Prof. Shilpa Gite, Student. M-tech (Computer Science), CSIT Department, SIU, Pune, Maharashtra, India.: Personalized Travel Recommendation Based on Hybrid Approach. In IJREAM (2016)

  • RahelehHassannia, Ali VatankhahBarenji, Zhi Li, Habib Alipour.: Web- Based Recommendation System for Smart Tourism: - Multiagent Technology. 10 January 2019. In Multidisciplinary Digital Publishing Institute Journal (2019)

  • ZhiyangJia, Wei Gao, Yuting Yang, Xu Chen: User-based Collaborative Filtering for Tourist Attraction Recommendations. 24 May 2016. In Research-Gate

  • Rahmatina Ari Apriliana, Ahmad FathanHidayatullah, Almed.: Recommending Tourism Destination using Collaborative Filtering. In: International Conference on Information Technology and Digital Applications (2019)

  • Logesh Ravi and SubramaniyaswamyVairavasundaram.: A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users. Received 4 November 2015; Revised 18 January 2016; Accepted 31 January 2016 Academic Editor: Silvia Conforto Copyright © 2016 L. Ravi and S. Vairavasundaram

  • Prof. P.A. Manjare, Miss P.V. Ninawe, Miss M.L. Dabhire, Miss R.S. Bonde, Miss D.S. Charhate, and Miss M.S. Gawande.: Recommendation System Based on Tourist Attraction. Published in the year 2016-April. Volume - 03, Issue -04, ISO 9001:2008 Certified Journal. In: International Research Journal of Engineering and Technology (IRJET)

  • Prof. P.A. Manjare, Miss P.V. Ninawe, Miss M.L. Dabhire, Miss R.S. Bonde, Miss D.S. Charhate, and Miss M.S. Gawande.: Recommendation System Based on Tourist Attraction. Published in the year 2016-April. Volume - 03, Issue -04, ISO 9001:2008 Certified Journal. In: International Research Journal of Engineering and Technology (IRJET)

  • Filipe Santos, Ana Almeida, ConstantinoMartins, PauloOliveira, RamiroGonçalves.: Tourism Recommendation System based on user’s profile and functionality level. In C3S2E '16, July 20-22, 2016, Porto, Portugal

  • HendAlrasheed, ArwaAlzeer, ArwaAlhowimel, Nora shameri, Aisha Althyabi.: A Multi-Level Tourism Destination Recommender System. In The 11th International Conference on Ambient Systems, Networks and Technologies (ANT), April 6 - 9, 2020, Warsaw, Poland

  • Prof SachinWalunj, YashwantBhaidkar, Pranav Bhagwat, Priyanka Bhalere, Radhika Gujar.: Tourist Place Recommendation System. Vol-2 Issue-3 2016. In: IJARIIE-ISSN

  • Liangliang Cao, Jiebo Luo, Andrew Gallagher, Xin Jin, Jiawei Han, and Thomas S. Huang.: A Worldwide Tourism Recommendation System Based on Geotagged Web Photos. In Beckman Institute and Coordinated Science Lab, Dept. ECE, UIUC, Kodak Research Laboratories, Eastman Kodak Company, Dept. Computer Science, the University of Illinois at Urbana- Champaign

  • ArtemUmanetsa, Artur Ferreira, NunoLeitea.: GuideMe - A Tourist Guide with a Recommender System and Social Interaction. In: Conference on Electronics, Telecommunications and Computers – CETC 2013, Lisboa, Portugal


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  • Travelopia: A Tourism Recommendation System

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Authors

S. S. Divyashree
Department of CSE, Global Academy of Technology, Bengaluru, India
K. Harshitha
Department of CSE, Global Academy of Technology, Bengaluru, India
Pradnya Hukkeri
Department of CSE, Global Academy of Technology,Bengaluru, India
A. M. Sharadhi
Department of CSE, Global Academy of Technology, Bengaluru, India
Bhagyshri R Hanji
HOD, Department of CSE, Global Academy of Technology, Bengaluru, India

Abstract


Recommendation Systems are one of the most appropriate applications of machine learning. They are the cluster of simple algorithms that provide the most significant and precise data as per the user's need. The Tourism domain is one of the predominant economic areas of a nation and the second-largest foreign exchange earner for India. They find relationships between users based on their actions, without any human curation involved at all. Travel Recommendation System (TRS) is a recommendation system used by tourists and travelers to fulfill their needs and make decisions about travel destinations, Points of Interest, restaurants, etc. The System Helps in making the tourism industry a "Smart Tourism Industry".

Keywords


Recommendation System, Filtering Approach, Association Rule Learning, Feature Selection.

References