Tourism Recommender System using Weighted Parallel Hybrid Method with Singular Value Decomposition
DOI:
https://doi.org/10.34818/INDOJC.2021.6.2.579Keywords:
Tourism Recommender System, Collaborative Filtering, Content-Based Filtering, Hybrid Method, Singular Value Decomposition, Weighted TechniqueAbstract
Presently, we often get suggestions for recommendations for tourist attractions from various sources such as the internet, magazines, newspapers, or travel agencies. Because there is numerous information, tourists become difficult to determine the tourism destination that suits their wishes. We created a tourism recommender system that can provide information in the form of recommendations for tourist attractions by the preference of tourists. The method used is a hybrid method that combines several recommendation methods, which are Content-Based Filtering (CB) and Collaborative Filtering (CF). We use tourism data of Lombok Island, West Nusa Tenggara, which will be taken from the TripAdvisor site. We apply the Singular Value Decomposition algorithm on CF and CB. The Hybrid Weighted Parallel Technique is used for Hybrid Method. The results of the experiment show that the weighting technique hybrid method provides higher prediction accuracy than when undergoing the recommender system method separately. The average results of Mean Square Error were obtained 0.7275 (CF), 0 .4583 (CB), and 0.2548 (Hybrid Method). The result indicates that the Hybrid Method with the Weighting Technique has the highest accuracy of another method.
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References
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[2] Lutfi Ambarwati and Z.K.A Baizal, “Group Recommender System Using Hybrid Filtering for Tourism Domain,†Indonesia Journal on Computing (Indo-JC), vol. 4, no. 2, pp. 21–30, Sep. 2019.
[3] Q. R. Arvianti and Z.K.A. Baizal, “Tourism Recommender System Using Item-Based Hybrid Clustering Method (Case Study: Bandung Raya Region),†Journal of Data Science and Its Applications, vol. 2, no. 2, pp. 95–101, Nov. 2019.
[4] S. Sharma, A. Sharma, Y. Sharma, and M. Bhatia, “Recommender system using hybrid approach,†2016 International Conference on Computing, Communication and Automation (ICCCA), 2016.
[5] R. Barathy and P. Chitra, “Applying Matrix Factorization In Collaborative Filtering Recommender Systems,†2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), 2020.
[6] R. Subramaniam, R. Lee, and T. Matsuo, “Movie Master: Hybrid Movie Recommendation,†2017 International Conference on Computational Science and Computational Intelligence (CSCI), 2017.
[7] O. Oyebode and R. Orji, “A hybrid recommender system for product sales in a banking environment,†Journal of Banking and Financial Technology, vol. 4, no. 1, pp. 15–25, 2020.
[8] R. Turnip, D. Nurjanah, and D. S. Kusumo, “Hybrid recommender system for learning material using content-based filtering and collaborative filtering with good learners rating,†2017 IEEE Conference on e-Learning, e-Management and e-Services (IC3e), 2017.
[9] M. Aprilianti, R. Mahendra, and I. Budi, “Implementation of weighted parallel hybrid recommender systems for e-commerce in Indonesia,†2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS), 2016.
[10] P. Darshna, “Music recommendation based on content and collaborative approach & reducing cold start problem,†2018 2nd International Conference on Inventive Systems and Control (ICISC), 2018.
[11] S. Rahmawati, D. Nurjanah, and R. Rismala, “Analisis dan Implementasi pendekatan Hybrid untuk Sistem Rekomendasi Pekerjaan dengan Metode Knowledge Based dan Collaborative Filtering,†Indonesian Journal on Computing (Indo-JC), vol. 3, no. 2, p. 11, 2018. [12] S. Rajarajeswari, S. Naik, S. Srikant, M. K. S. Prakash, and P. Uday, “Movie Recommendation System,†Emerging Research in Computing, Information, Communication and Applications Advances in Intelligent Systems and Computing, pp. 329–340, 2019.
[13] N. Nassar, A. Jafar, and Y. Rahhal, “A novel deep multi-criteria collaborative filtering model for recommendation system,†Knowledge-Based Systems, vol. 187, p. 104811, 2020.
[14] S. K. Raghuwanshi and R. K. Pateriya, “Collaborative Filtering Techniques in Recommendation Systems,†Data, Engineering and Applications, pp. 11–21, 2019.
[15] J. Wei, J. He, K. Chen, Y. Zhou, and Z. Tang, “Collaborative filtering and deep learning based recommendation system for cold start items,†Expert Systems with Applications, vol. 69, pp. 29–39, 2017.
[16] J. Jooa, S. Bangb, and G. Parka, “Implementation of a Recommendation System Using Association Rules and Collaborative Filtering,†Procedia Computer Science, vol. 91, pp. 944–952, 2016.
[17] G. Suganeshwari and S. P. S. Ibrahim, “A Survey on Collaborative Filtering Based Recommendation System,†Proceedings of the 3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC – 16’) Smart Innovation, Systems and Technologies, pp. 503–518, 2016.
[18] V. Subramaniyaswamy, R. Logesh, M. Chandrashekhar, A. Challa, and V. Vijayakumar, “A personalised movie recommendation system based on collaborative filtering,†International Journal of High Performance Computing and Networking, vol. 10, no. 1/2, p. 54, 2017.
[19] P. Mathew, B. Kuriakose, and V. Hegde, “Book Recommendation System through content based and collaborative filtering method,†2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), 2016.
[20] F. Isinkaye, Y. Folajimi, and B. Ojokoh, “Recommendation systems: Principles, methods and evaluation,†Egyptian Informatics Journal, vol. 16, no. 3, pp. 261–273, 2015.
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Published
2021-09-28
How to Cite
Akbar, Y. A., baizal, zk abdurahman, & Wibowo, A. T. (2021). Tourism Recommender System using Weighted Parallel Hybrid Method with Singular Value Decomposition. Indonesian Journal on Computing (Indo-JC), 6(2), 53–64. https://doi.org/10.34818/INDOJC.2021.6.2.579
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Computer Science
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