The HYBRID CONTENT-BASED FILTERING AND CLASSIFICATION RNN WITH PARTICLE SWARM OPTIMIZATION FOR TOURISM RECOMMENDATION SYSTEM
Abstract
Economic recovery in the tourism sector after the COVID-19 pandemic is one of the main focuses of the Indonesian government at the moment, especially in Bandung City. This research aims to develop a personalized tourist spot recommendation system, by addressing the gaps in the existing literature through the integration of Content-Based Filtering (CBF) and Simple Recurrent Neural Network (RNN) methods that aim to improve recommendation accuracy. This study uses a hybrid approach that combines Term Frequency - Inverse Document Frequency (TF-IDF) and word embedding with the Robustly Optimized BERT (RoBERTa) model to identify similarities between tourist destinations based on their content characteristics. Simple RNN is used to analyze user preference patterns over time, which is then further optimized using Particle Swarm Optimization (PSO). As a result, the Simple RNN model that has been optimized with PSO shows an increased accuracy of up to 94.37%, outperforming other optimizations such as Adam and SGD. This research makes a novel contribution by applying advanced machine learning techniques to improve personalization in travel recommendation systems.
Downloads
References
Badan Keahlian Sekretariat Jenderal DPR RI, “Analisis Ringkas Cepat: Urgensi Penguatan Daya Saing Pariwisata Untuk Meningkatkan Perekonomian Nasional,” Www.Bk.Dpr.Go.Id, pp. 1–44, 2023, [Online]. Available: https://berkas.dpr.go.id/pa3kn/analisis-ringkas-cepat/public-file/analisis-ringkas-cepat-public-41.pdf.
B. Utami, “SEKTOR PARIWISATA INDONESIA DI TENGAH PANDEMI COVID 19,” J. Din. Ekon. Pembang., vol. 4, 2021, doi: 10.33005/jdep.v4i1.198.
I. M. Hasibuan, S. Mutthaqin, R. Erianto, and I. Harahap, “Kontribusi Sektor Pariwisata terhadap Perekonomian Nasional,” J. Masharif Al-Syariah J. Ekon. dan Perbank. Syariah, vol. 8, no. 2, 2023, doi: 10.30651/jms.v8i2.19280.
F. Ricci, “Recommender Systems in Tourism,” in Handbook of e-Tourism, Z. Xiang, M. Fuchs, U. Gretzel, and W. Höpken, Eds. Cham: Springer International Publishing, pp. 457–474, 2022.
N. Abhinav and K. Sujatha, “Content-based movie recommendation system using cosine similarity measure,” AIP Conf. Proc., vol. 2901, no. 1, p. 60035, 2023, doi: 10.1063/5.0178819.
A. A. Salsabil, E. B. Setiawan, and I. Kurniawan, “Content-based Filtering Movie Recommender System Using Semantic Approach with Recurrent Neural Network Classification and SGD,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 9, no. 2, pp. 193–202, May 2024, doi: 10.22219/kinetik.v9i2.1940.
I. M. Al Jawarneh et al., “A Pre-Filtering Approach for Incorporating Contextual Information into Deep Learning Based Recommender Systems,” IEEE Access, vol. 8, pp. 40485–40498, 2020, doi: 10.1109/ACCESS.2020.2975167.
D. Magdaleno, M. Montes, B. Estrada, and A. Ochoa-Zezzatti, “A GPT-Based Approach for Sentiment Analysis and Bakery Rating Prediction,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 14502 LNAI, no. February, pp. 61–76, 2024, doi: 10.1007/978-3-031-51940-6_7.
L. Li, Z. Zhang, and S. Zhang, “Hybrid Algorithm Based on Content and Collaborative Filtering in Recommendation System Optimization and Simulation,” Sci. Program., vol. 2021, pp. 1–11, 2021, doi: 10.1155/2021/7427409.
L. Xiaoyan, R. C. Raga, and S. Xuemei, “GloVe-CNN-BiLSTM Model for Sentiment Analysis on Text Reviews,” J. Sensors, vol. 2022, 2022, doi: 10.1155/2022/7212366.
P. R. Amalia and E. Winarko, “Aspect-Based Sentiment Analysis on Indonesian Restaurant Review Using a Combination of Convolutional Neural Network and Contextualized Word Embedding,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 15, no. 3, p. 285, 2021, doi: 10.22146/ijccs.67306.
N. Hossain, M. R. Bhuiyan, Z. N. Tumpa, and S. A. Hossain, “Sentiment Analysis of Restaurant Reviews using Combined CNN-LSTM,” 2020 11th Int. Conf. Comput. Commun. Netw. Technol. ICCCNT 2020, no. July, 2020, doi: 10.1109/ICCCNT49239.2020.9225328.
G. Yunanda, D. Nurjanah, and S. Meliana, “Recommendation System from Microsoft News Data using TF-IDF and Cosine Similarity Methods,” Build. Informatics, Technol. Sci., vol. 4, no. 1, pp. 277–284, 2022, doi: 10.47065/bits.v4i1.1670.
M. Chiny, M. Chihab, O. Bencharef, and Y. Chihab, “Netflix Recommendation System based on TF-IDF and Cosine Similarity Algorithms,” Proc. 2nd Int. Conf. Big Data, Model. Mach. Learn, pp.15-20, 2021.
K. L. Tan, C. P. Lee, and K. M. Lim, “RoBERTa-GRU: A Hybrid Deep Learning Model for Enhanced Sentiment Analysis,” Appl. Sci., vol. 13, no. 6, 2023, doi: 10.3390/app13063915.
J. J. Jiang, W. X. Wei, W. L. Shao, Y. F. Liang, and Y. Y. Qu, “Research on Large-Scale Bi-Level Particle Swarm Optimization Algorithm,” IEEE Access, vol. 9, pp. 56364–56375, 2021, doi: 10.1109/ACCESS.2021.3072199.
Z. Cao, X. Han, W. Lyons, and F. O’Rourke, “Energy management optimisation using a combined Long Short-Term Memory recurrent neural network – Particle Swarm Optimisation model,” J. Clean. Prod., vol. 326, no. May, p. 129246, 2021, doi: 10.1016/j.jclepro.2021.129246.
A. Javadian Sabet, M. Shekari, C. Guan, M. Rossi, F. Schreiber, and L. Tanca, “THOR: A Hybrid Recommender System for the Personalized Travel Experience,” Big Data Cogn. Comput., vol. 6, no. 4, 2022, doi: 10.3390/bdcc6040131.
S. P. R. Asaithambi, R. Venkatraman, and S. Venkatraman, “A Thematic Travel Recommendation System Using an Augmented Big Data Analytical Model,” Technologies, vol. 11, no. 1, 2023, doi: 10.3390/technologies11010028.
J. H. Yoon and C. Choi, “Real-Time Context-Aware Recommendation System for Tourism,” Sensors, vol. 23, no. 7, 2023, doi: 10.3390/s23073679.
J. Lee, J. A. Shin, D. K. Chae, and S. C. Lee, “Personalized Tour Recommendation via Analyzing User Tastes for Travel Distance, Diversity and Popularity,” Electron., vol. 11, no. 7, pp. 1–14, 2022, doi: 10.3390/electronics11071120.
M. Sigala, “Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID- 19 . The COVID-19 resource centre is hosted on Elsevier Connect , the company ’ s public news and information ,” no. January, 2020.
S. Babichev, “applied sciences Applying a Recurrent Neural Network-Based Deep Learning Model for Gene Expression Data Classification,” Applied Sciences, vol 13 no 21, pp. 11823, 2023.
H. Yang, C. Jiang, Y. Song, W. Fan, Z. Deng, and X. Bai, “TARGCN: temporal attention recurrent graph convolutional neural network for traffic prediction,” Complex Intell. Syst., 2024, doi: 10.1007/s40747-024-01601-1.
M. Zitnik et al., “Systems biology Current and future directions in network biology,” arXiv preprint arXiv, pp. 2309.08478, 2024.
S. Lun, Z. Sun, M. Li, and L. Wang, “Multiple-Reservoir Hierarchical Echo State Network,” Mathematics, vol. 11, no. 18, pp. 3961, 2023.
E. Chammas, “Fine-tuning Handwriting Recognition systems with Temporal Dropout.”, arXiv preprint arXiv, pp. 2102.00511, 2021
V. Shatravin, D. Shashev, and S. Shidlovskiy, “Sigmoid Activation Implementation for Neural Networks Hardware Accelerators Based on Reconfigurable Computing Environments for Low-Power Intelligent Systems,” Appl. Sci., vol. 12, no. 10, 2022, doi: 10.3390/app12105216.
J. H. Joloudari, A. Marefat, M. A. Nematollahi, S. S. Oyelere, and S. Hussain, “Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks,” Appl. Sci., vol. 13, no. 6, 2023, doi: 10.3390/app13064006.
E. Hassan, M. Y. Shams, N. A. Hikal, and S. Elmougy, The effect of choosing optimizer algorithms to improve computer vision tasks: a comparative study, vol. 82, no. 11. Multimedia Tools and Applications, pp. 16591-16633 2023.
Y. Fan, Y. Zhang, B. Guo, X. Luo, and Q. Peng, “A Hybrid Sparrow Search Algorithm of the Hyperparameter Optimization in Deep Learning,” Mathematics, vol. 10, no. 16 pp. 3019, 2022.
Copyright (c) 2024 Syahdan Naufal Nur Ihsan, Erwin Budi Setiawan
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
-a.jpg)
-b.jpg)
