Possible System Architecture for Travel Recommender

Authors

  • Supriyanto Supriyanto Informatics Department, Universitas Ahmad Dahlan, Indonesia http://orcid.org/0000-0001-9330-0683
  • Jefree Fahana Informatics Department, Universitas Ahmad Dahlan, Indonesia

DOI:

https://doi.org/10.15575/join.v5i1.573

Keywords:

Collaborative Filtering, Content-based, Knowledge-based, System Architecture, Travel Recommender

Abstract

Travel recommender systems have been developed to meet the needs of users in the field of tourism. This system has several versions depending on the characteristics of the country, users and filtering techniques used. The development of recommendation filtering system techniques is very rapid so that the recommendation system has high enough complexity, but it also must have high usability. This paper discusses how the travel recommender system architecture is built by examining data structures, processing procedures and interaction design. The goal is to obtain the best usability in implementing a travel recommendation system. The system is built using the example case of finding the right tourist spot in Yogyakarta, Indonesia. This system applies several filtering techniques such as knowledge-based filtering, content-based filtering, and collaborative filtering. The evaluation results show that the system architecture optimized gets a usability level acceptable.

References

Y. Li, C. Hu, C. Huang, and L. Duan, “The concept of smart tourism in the context of tourism information services,†Tour. Manag., vol. 58, pp. 293–300, 2017, doi: 10.1016/j.tourman.2016.03.014.

Supriyanto, J. Fahana, and S. Handoko, “Gamification to Improve Digital Data Collection in Ecotourism Management,†in 2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT), 2018.

L. Kzaz, D. Dakhchoune, and D. Dahab, “Tourism Recommender Systems: An Overview of Recommendation Approaches,†Int. J. Comput. Appl., vol. 180, no. 20, pp. 9–13, 2018, doi: 10.5120/ijca2018916458.

R. Hassannia, A. V. Barenji, Z. Li, and H. Alipour, “Web-based recommendation system for smart tourism: Multiagent technology,†Sustain., vol. 11, no. 2, 2019, doi: 10.3390/su11020323.

S. Jain, A. Grover, P. S. Thakur, and S. K. Choudhary, “Trends, problems and solutions of recommender system,†Int. Conf. Comput. Commun. Autom. ICCCA 2015, no. May, pp. 955–958, 2015, doi: 10.1109/CCAA.2015.7148534.

Sangeeta and N. Duhan, “Collaborative filtering-based recommender system,†in Advances in Intelligent Systems and Computing, 2018, doi: 10.1007/978-981-10-6602-3_19.

T. De Pessemier, J. Dhondt, K. Vanhecke, and L. Martens, “TravelWithFriends: a Hybrid Group Recommender System for Travel Destinations,†9th ACM Conf. Recomm. Syst., 2015.

L. Roopesh and B. Tulasi, “A Survey of Travel Recommender system,†Int. J. Comput. Sci. Eng., vol. 6, no. 9, 2015.

J. Borrà s, A. Moreno, and A. Valls, “Intelligent tourism recommender systems: A survey,†Expert Syst. Appl., vol. 41, no. 16, pp. 7370–7389, 2014, doi: 10.1016/j.eswa.2014.06.007.

M. H. Mohamed, M. H. Khafagy, and M. H. Ibrahim, “Recommender Systems Challenges and Solutions Survey,†2019 Int. Conf. Innov. Trends Comput. Eng., no. February, pp. 149–155, 2019, doi: 10.1109/ITCE.2019.8646645.

L. Sharma and A. Gera, “A Survey of Recommendation System : Research Challenges,†Int. J. Eng. Trends Technol., vol. 4, no. 5, pp. 1989–1992, 2013.

C. C. Aggarwal, “Knowledge-Based Recommender Systems,†in Recommender Systems, 2016.

R. Burke, “Knowledge-based recommender systems,†University of California, 2017.

J. K. Tarus, Z. Niu, and G. Mustafa, “Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning,†Artif. Intell. Rev., vol. 50, no. 1, pp. 21–48, 2018, doi: 10.1007/s10462-017-9539-5.

A. Ameen, “Knowledge based Recommendation System in Semantic Web - A Survey,†Int. J. Comput. Appl., vol. 182, no. 43, pp. 20–25, 2019, doi: 10.5120/ijca2019918538.

F. Ricci, B. Shapira, and L. Rokach, “Recommender systems handbook, Second edition,†Recomm. Syst. Handbook, Second Ed., pp. 1–1003, 2015, doi: 10.1007/978-1-4899-7637-6.

A. Felfernig, S. Haas, G. Ninaus, M. Schwarz, T. Ulz, and M. Stettinger, “RecTurk: Constraint-based Recommendation based on Human Computation,†in ACM Recommender Systems 2014 - CrowdRec Workshop, 2014.

Q. Chen et al., “Towards Knowledge-Based Recommender Dialog System,†in 2019 Conference on Empirical Methods in Natural Language Processingand the 9th International Joint Conference on Natural Language Processing, 2019, pp. 1803–1813, doi: 10.18653/v1/d19-1189.

R. Wita, K. Bubphachuen, and J. Chawachat, “Content-Based Filtering Recommendation in Abstract Search Using Neo4j,†ICSEC 2017 - 21st Int. Comput. Sci. Eng. Conf. 2017, Proceeding, vol. 6, pp. 136–139, 2018, doi: 10.1109/ICSEC.2017.8443957.

Y. Afoudi, M. Lazaar, and M. Al Achhab, “Collaborative filtering recommender system,†in Advances in Intelligent Systems and Computing, 2019, doi: 10.1007/978-3-030-11928-7_30.

T. Tran, “Combining Collaborative Filtering and Knowledge-Based Approaches for Better Recommendation Systems,†J. Bus. Technol., 2007.

Supriyanto, A. Prahara, and T. Susanto, “Keystroke-Level Model to Evaluate Chatbot Interface for Reservation System,†in 6th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI 2019), 2019.

A. Assila, K. De Oliveira, and H. Ezzedine, “Standardized Usability Questionnaires: Features and Quality Focus,†J. Comput. Sci. Inf. Technol., vol. 6, no. 1, pp. 15–31, 2016.

S. Kaur, K. Kaur, and P. Kaur, “Analysis of website usability evaluation methods,†in Proceedings of the 10th INDIACom; 2016 3rd International Conference on Computing for Sustainable Global Development, INDIACom 2016, 2016.

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2020-07-16

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