Non-Rating Recommender System for Choosing Tourist Destinations Using Artificial Neural Network

Yunifa Miftachul Arif, Dyah Wardani, Hani Nurhayati, Norizan Mat Diah

Abstract


The development of tourist destinations in Batu City makes it hard for the tourists to decide their destinations. The recommender system is a solution that provides a lot of information or tourist attraction data. Collaborative filtering is often used in recommender systems. However, it has drawbacks; one of which is the cold-start problem, where the system cannot recommend items to new users. It was caused by the new user who had no history of rating on any item, or the system had no information. This study aims to apply a non-rated travel destination recommendation system to address the cold-start problem for new users. We use a multi-layer perceptron or artificial neural networks to overcome the problem by training user preference data to produce high training accuracy. Based on four experiments in the training data, the network architecture shows 5 – 7 – 5 – 3 –14, which is the highest accuracy. The architecture uses five variables as inputs and three hidden layers, with each layer was activated using the ReLU activation function. The output layer produces 14 binary outputs and is activated using the sigmoid activation function. The system can give recommendations to new users using feedforward from test data with updated values in weights and biases. The test results from 46 test data showed an accuracy of 67.235%.

Keywords


Cold-start problem, artificial neural network, recommendation system, collaborative filtering

Full Text:

PDF

References


M. S. Mahfudz, Z. Arham, and E. Khudzaeva, “Development of web-based spatial information system tourism industry event distribution (case study),” Applied Information Systems and Management (AISM), vol. 3, no. 2, pp. 107–112, October 2020.

M. Antara and M. S. Sumarniasih, “Role of tourism in economy of bali and indonesia,” J. Tour. Hosp. Manag., vol. 5, no. 2, 2017, doi: 10.15640/jthm.v5n2a4.

[E. Yunus and M. Indrasari, “Opportunities and challenges of tourism industry,” Int. J. Econ. Res., vol. 14, no. 7, pp. 277–291, 2017.

Y. D. Mahendra, “Yogyakarta tourism object recommendation system with item-based collaborative filtering approach,” Sanata Dharma, 2018.

T. N. Trang Tran, M. Atas, A. Felfernig, and M. Stettinger, “An overview of recommender systems in the healthy food domain,” J. Intell. Inf. Syst., vol. 50, no. 3, pp. 501–526, 2018, doi: 10.1007/s10844-017-0469-0.

A. D. Putri and A. Susanto, “Hobby-based friendship recommendation system using the multicriteria decision making method,” J. Inform. dan Rekayasa Perangkat Lunak, vol. 2, no. 1, pp. 1–6, 2020, doi: 10.36499/jinrpl.v2i1.2787.

S. Das Barman, M. Hasan, and F. Roy, “A genre-based item-item collaborative filtering: facing the cold-start problem,” Proc. 2019 8th Int. Conf. Softw. Comput. Appl. (ICSCA ’19), vol. Part F1479, pp. 258–262, 2019, doi: 10.1145/3316615.3316732.

A. V. Prando, F. G. Contratres, S. N. A. Souza, and L. S. De Souza, “Content-based recommender system using social networks for cold-start users,” IC3K 2017 - Proc. 9th Int. Jt. Conf. Knowl. Discov. Knowl. Eng. Knowl. Manag., vol. 1, no. January, pp. 181–189, 2017, doi: 10.5220/0006496301810189.

B. Selva Rani and S. Ananda Kumar, “Mitigating cold start problem in a personalized recommender system,” Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 7, pp. 212–216, 2019.

S. Sidana, M. Trofimov, O. Horodnytskyi, C. Laclau, Y. Maximov, and M. R. Amini, “User preference and embedding learning with implicit feedback for recommender systems,” Data Min. Knowl. Discov., vol. 35, no. 2, pp. 568–592, 2021, doi: 10.1007/s10618-020-00730-8.

J. Deng, J. Guo, and Y. Wang, “A novel k-medoids clustering recommendation algorithm based on probability distribution for collaborative filtering,” Knowledge-Based Syst., vol. 175, pp. 96–106, 2019, doi: 10.1016/j.knosys.2019.03.009.

Y. M. Arif, H. Nurhayati, F. Kurniawan, S. M. S. Nugroho, and M. Hariadi, “Blockchain-based data sharing for decentralized tourism destinations recommendation system,” Int. J. Intell. Eng. Syst., vol. 13, no. 6, pp. 472–486, 2020, doi: 10.22266/ijies2020.1231.42.

O. I. Abiodun, A. Jantan, A. E. Omolara, K. V. Dada, N. A. E. Mohamed, and H. Arshad, “State-of-the-art in artificial neural network applications: A survey,” Heliyon, vol. 4, no. 11, p. e00938, 2018, doi: 10.1016/j.heliyon.2018.e00938.

A. Santoso and S. Hansun, “JCI prediction with backpropagation neural network,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 3, no. 2, pp. 313–318, 2019, doi: 10.29207/resti.v3i2.887.

D. D. Putra, “Tourism destination recommendation system in batu city using the multi criteria recommender system method based on the 6AsTD framework,” Undergraduate Thesis, UIN Maulana Malik Ibrahim Malang, 2021.

O. S. Bachri, Kusnadi, M. Hatta, and O. D. Nurhayati, “Feature selection based on CHI square in artificial neural network to predict the accuracy of student study period,” Int. J. Civ. Eng. Technol., vol. 8, no. 8, pp. 731–739, 2017.

P. Koopman, K. Driscoll, and B. Hall, “Selection of Cyclic Redundancy Code and Checksum Algorithms to Ensure Critical Data Integrity,” Dot/Faa/Tc-14/49, no. March, p. 111, 2015, [Online]. Available: https://www.abbottaerospace.com/downloads/dot-faa-tc-14-49-selection-of-cyclic-redundancy-code-and-checksum-algorithms-to-ensure-critical-data-integrity/

T. Wang, T. Wei, G. Gu, and W. Zou, “Checksum-aware fuzzing combined with dynamic taint analysis and symbolic execution,” ACM Trans. Inf. Syst. Secur., vol. 14, no. 2, pp. 1–28, 2011, doi: 10.1145/2019599.2019600.

D. J. Montana and L. Davis, “Training Feedforward Neural Networks Using Genetic Algorithms,” in Proceedings of the 11th International Joint Conference on Artificial intelligence - Volume 1, 1989, vol. 89, pp. 762–767. [Online]. Available: http://dl.acm.org/citation.cfm?id=1623755.162

M. D. McDonnell, M. D. Tissera, T. Vladusich, A. Van Schaik, J. Tapson, and F. Schwenker, “Fast, simple and accurate handwritten digit classification by training shallow neural network classifiers with the ‘Extreme learning machine’ algorithm,” PLoS One, vol. 10, no. 8, p. e0134254, 2015, doi: 10.1371/journal.pone.0134254.

Y. Oh, S. Park, and J. C. Ye, “Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets,” IEEE Trans. Med. Imaging, vol. 39, no. 8, pp. 2688–2700, 2020, doi: 10.1109/TMI.2020.2993291.

Y. Oh, S. Park, and J. C. Ye, “Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets,” IEEE Trans. Med. Imaging, vol. 39, no. 8, pp. 2688–2700, 2020, doi: 10.1109/TMI.2020.2993291.




DOI: https://doi.org/10.15408/aism.v6i2.26741 Abstract - 0 PDF - 0

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

EDITORIAL ADDRESS:

Department of Information Systems, Faculty of Science and Technology,
Universitas Islam Negeri (UIN) Syarif Hidayatullah Jakarta
Faculty of Science and Technology Building, 3rd Floor, 1st Campus, Universitas Islam Negeri (UIN) Syarif Hidayatullah Jakarta
Jl. Ir. H. Juanda No. 95, Ciputat Timur, Kota Tangerang Selatan, Banten 15412, Indonesia.
Tlp/Fax: +622174019 25/+62217493315.
E-mail: aism.journal@apps.uinjkt.ac.id, Website: https://journal.uinjkt.ac.id/index.php/aism

Creative Commons Licence
Applied Information System and Management (AISM) by the Department of Information Systems, Faculty of Science and Technology, Universitas Islam Negeri (UIN) Syarif Hidayatullah Jakarta, Indonesia is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://journal.uinjkt.ac.id/index.php/aism.