Tourist Places Recommender System Using Cosine Similarity and Singular Value Decomposition Methods
DOI:
https://doi.org/10.30865/mib.v5i4.3151Keywords:
Tourism, Recommendation, Cosine Similarity, Singular Value Decomposition, Rating, ReviewAbstract
Tourism in the city of Bandung has various potentials in the field of culture, regional specialties, buildings, and other tourist attractions. On the Tripadvisor page there are many reviews from users who have visited tourist attractions in the city of Bandung. In this case, user reviews are an important element for analysis. The analysis process is carried out using rule-based sentiment analysis. In conducting the review analysis, we use vaderSentiment to weight the positive and negative values. Positive values are subtracted from negative values to get a compound value and converted to a rating value. The rating value obtained is then processed using the Cosine Similarity and Singular Value Decomposition methods to obtain recommendations for tourist attractions in the city of Bandung. For this method, we use the Root Mean Square Error method as a measure of the level of accuracy between the predicted values. The results of the measurement of the level of accuracy produce a value of 3,489 in the Cosine Similarity method, while the Singular Value Decomposition method gets a value of 1,231. The value in the Singular Value Decomposition method is smaller than the Cosine Similarity method with a difference of 2,258 valuesReferences
BPS-Statistics Indonesia, "Domestic Tourism Statistics 2019", BPS RI / BPS-Statistics Indonesia, 2019.
Utami, D. D., Sinaga, E. K., Desiria, M. K., Febriani, N., Prayitno, R. A., Department, T., Tinggi, S., & Bandung, P. (2020). Potential of Smart Tourism Destination in Bandung City. TEST Engineering and Management.
Fajri, K., & Riyanto E.S, N. (2016). STRATEGI PENGEMBANGAN DESTINASI PARIWISATA KOTA BANDUNG DALAM MENINGKATKAN TINGKAT KUNJUNGAN WISATAWAN ASAL MALAYSIA. Tourism Scientific Journal. https://doi.org/10.32659/tsj.v1i2.9
Xie, K. L., Chen, C., & Wu, S. (2016). Online Consumer Review Factors Affecting Offline Hotel Popularity: Evidence from Tripadvisor. Journal of Travel and Tourism Marketing. https://doi.org/10.1080/10548408.2015.1050538
Vagliano, I., Monti, D., & Morisio, M. (2017). SemRevRec: A recommender system based on user reviews and linked data. CEUR Workshop Proceedings.
Al-Ghuribi, S. M., & Mohd Noah, S. A. (2019). Multi-Criteria Review-Based Recommender System-The State of the Art. In IEEE Access. https://doi.org/10.1109/ACCESS.2019.2954861
Zhao, Q., Zhang, Y., Ma, J., & Duan, Q. (2019). Factored Item Similarity and Bayesian Personalized Ranking for Recommendation with Implicit Feedback. Arabian Journal for Science and Engineering. https://doi.org/10.1007/s13369-018-3358-0
Ardimansyah, M. I., Huda, A. F., & Baizal, Z. K. A. (2017). Preprocessing matrix factorization for solving data sparsity on memory-based collaborative filtering. Proceeding - 2017 3rd International Conference on Science in Information Technology: Theory and Application of IT for Education, Industry and Society in Big Data Era, ICSITech 2017. https://doi.org/10.1109/ICSITech.2017.8257168
Hug, N. (2020). Surprise: A Python library for recommender systems. Journal of Open Source Software. https://doi.org/10.21105/joss.02174
Gutflaish, E., Kontorovich, A., Sabato, S., Biller, O., & Sofer, O. (2019). Temporal anomaly detection: Calibrating the surprise. 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019. https://doi.org/10.1609/aaai.v33i01.33013755
Sageri Fikri Ramadhan, ZK Abdurahman Baizal and Rita Rismala.(2020). Lodging Recommendations Using the SparkML Engine ALS and Surprise SVD. Jurnal Media Informatika Budidarma. https://doi.org/10.30865/mib.v%25vi%25i.2257.
Kumar, A., Seth, S., Gupta, S., & Shubham. (2020). Sentiment-enhanced content-based system for online recommendations and rating prediction. International Journal of Gaming and Computer-Mediated Simulations. https://doi.org/10.4018/IJGCMS.2020040101
Joy, J., & Renumol, V. G. (2020). Comparison of generic similarity measures in E-learning content recommender system in cold-start condition. 2020 IEEE Bombay Section Signature Conference, IBSSC 2020. https://doi.org/10.1109/IBSSC51096.2020.9332162
Protasiewicz, J., Pedrycz, W., Kozłowski, M., Dadas, S., Stanisławek, T., Kopacz, A., & Gałȩzewska, M. (2016). A recommender system of reviewers and experts in reviewing problems. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2016.05.041
Rodpysh, K. V., Mirabedini, S. J., & Banirostam, T. (2021). Employing singular value decomposition and similarity criteria for alleviating cold start and sparse data in context-aware recommender systems. Electronic Commerce Research. https://doi.org/10.1007/s10660-021-09488-7
Rahmawati, S., Nurjanah, D., & Rismala, R. (2018). Analisis dan Implementasi pendekatan Hybrid untuk Sistem Rekomendasi Pekerjaan dengan Metode Knowledge Based dan Collaborative Filtering. Indonesian Journal on Computing (Indo-JC). https://doi.org/10.21108/indojc.2018.3.2.210
Ćalasan, M., Abdel Aleem, S. H. E., & Zobaa, A. F. (2020). On the root mean square error (RMSE) calculation for parameter estimation of photovoltaic models: A novel exact analytical solution based on Lambert W function. Energy Conversion and Management. https://doi.org/10.1016/j.enconman.2020.112716
Wang, X., Zhang, F., Kung, H. te, Johnson, V. C., & Latif, A. (2020). Extracting soil salinization information with a fractional-order filtering algorithm and grid-search support vector machine (GS-SVM) model. International Journal of Remote Sensing. https://doi.org/10.1080/01431161.2019.1654142
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).