Trip Planner: A Big Data Analytics Based Recommendation System for Tourism Planning
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Abstract
Foreign tourism has gained immense popularity in the recent past. To make a rational decision about the destination to be visited one has to go through variety of social media sources with very large number of reviews, which is a tedious task. Automated analysis of these reviews is quite complex as it involves non structured text data having slang terms also. Moreover, these reviews are pouring in continuously. To overcome this problem, this paper provides a Big Data analytics-based framework to make appropriate selection of the destination on the basis of automated analysis of social media contents based upon the adaptation and augmentation of various tools and technologies. The framework has been implemented using Apache Spark and Bidirectional Encoder Representation Transformers (BERT) deep learning models through which raw text review are analysed and a final score based on five metrics is obtained to recommend destination for visit.
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References
https://www.bbc.co.uk/bitesize/guides/zqk7hyc/revision/1
http://www.walkthroughindia.com/around-the-world/top-15-most-visited-countries-by-indian-travellers/
https://www.selenium.dev/downloads/
https://pypi.org/project/selenium/
https://beautiful-soup-4.readthedocs.io/en/latest/
https://www.crummy.com/software/BeautifulSoup/bs4/doc/#installing-beautiful-soup
https://spark.apache.org/docs/latest/
https://ai.googleblog.com/2018/11/open-sourcing-bert-state-of-art-pre.html
Ana Reyes-Menendez , Jose Ramon Saura, And Juan Gabriel Martinez-Navalon, June, 2019, The impact of e-wom on hotels management Reputation: Exploring TripAdvisor Review Credibility with the ELM model, Translation And Content Mining, IEEE Access, Vol 7, 2019.
S. M. Al-Ghuribi, S. A. Mohd Noah and S. Tiun, "Unsupervised Semantic Approach of Aspect-Based Sentiment Analysis for Large-Scale User Reviews", IEEE Access, vol. 8, pp. 218592-218613, 2020., doi: 10.1109/ACCESS.2020.3042312.
K. S. Ntalianis, A. Kener and J. Otterbacher, "Feelings’ Rating and Detection of Similar Locations, Based on Volunteered Crowdsensing and Crowdsourcing," in IEEE Access, vol. 7, pp. 90215-90229, 2019, doi: 10.1109/ACCESS.2019.2926812.
Benlahbib and E. H. Nfaoui, "Aggregating Customer Review Attributes for Online Reputation Generation," in IEEE Access, vol. 8, pp. 96550-96564, 2020, doi: 10.1109/ACCESS.2020.2996805.
S. M. Al-Ghuribi and S. A. Mohd Noah, "Multi-Criteria Review-Based Recommender System–The State of the Art" in IEEE Access, vol. 7, pp. 169446-169468, 2019, doi: 10.1109/ACCESS.2019.2954861.
?. Topal and M. K. Uçar, "Hybrid Artificial Intelligence Based Automatic Determination of Travel Preferences of Chinese Tourists," in IEEE Access, vol. 7, pp. 162530-162548, 2019, doi: 10.1109/ACCESS.2019.2947712.
K. A. Fararni, F. Nafis, B. Aghoutane, A. Yahyaouy, J. Riffi and A. Sabri, "Hybrid recommender system for tourism based on big data and AI: A conceptual framework," in Big Data Mining and Analytics, vol. 4, no. 1, pp. 47-55, March 2021, doi: 10.26599/BDMA.2020.9020015.
G. Adomavicius and A. Tuzhilin, Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions, IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734– 749, 2005.
G. Sun, R. Liang, H. Qu and Y. Wu, "Embedding Spatio-Temporal Information into Maps by Route-Zooming," in IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 5, pp. 1506-1519, 1 May 2017, doi: 10.1109/TVCG.2016.2535234.
J. He and H. Hu, "MF-BERT: Multimodal Fusion in Pre-trained BERT for Sentiment Analysis," in IEEE Signal Processing Letters, doi: 10.1109/LSP.2021.3139856.
H. C. M. Senefonte, M. R. Delgado, R. Lüders and T. H. Silva, "PredicTour: Predicting Mobility Patterns of Tourists Based on Social Media User’s Profiles," in IEEE Access, vol. 10, pp. 9257-9270, 2022, doi: 10.1109/ACCESS.2022.3143503.
P. Nitu, J. Coelho and P. Madiraju, "Improvising personalized travel recommendation system with recency effects," in Big Data Mining and Analytics, vol. 4, no. 3, pp. 139-154, Sept. 2021, doi: 10.26599/BDMA.2020.9020026.
Y. Wang, M. Wang, and W. Xu, ``A sentiment-enhanced hybrid recommender system for movie recommendation: A big data analytics framework,'' Wireless Commun. Mobile Comput., vol. 2018, Mar. 2018, Art. no. 8263704.
S. Aciar, D. Zhang, S. Simoff, and J. Debenham, ``Informed recommender: Basing recommendations on consumer product reviews,'' IEEE Intell. Syst., vol. 22, no. 3, pp. 39_47, May/Jun. 2007.
L. Sun, J. Guo, and Y. Zhu, ``Applying uncertainty theory into the restaurant recommender system based on sentiment analysis of online Chinese reviews,'' World Wide Web, vol. 22, no. 1, pp. 83_100, 2018.
M. Siering, A. V. Deokar, and C. Janze, ``Disentangling consumer recommendations: Explaining and predicting airline recommendations based on online reviews,'' Decis. Support Syst., vol. 107, pp. 52_63, Mar. 2018.
R. Dong, M. P. O'Mahony, M. Schaal, K. McCarthy, and B. Smyth, ``Sentimental product recommendation,'' in Proc. 7th ACM Conf. Recommender Syst., 2013, pp. 411_414.
G. Chen and L. Chen, ``Recommendation based on contextual opinions,'' in Proc. Int. Conf. Modelling, Adaptation, Pers., 2014, pp. 61_73.
X. Zheng, Y. Luo, L. Sun, J. Zhang, and F. Chen, ``A tourism destination recommender system using users' sentiment and temporal dynamics,'' J. Intell. Inf. Syst., vol. 51, no. 3, pp. 557_578, 2018.
H. S. Indriany, A. N. Hidayanto, L. J. Wantania, B. Santoso, W. U. Putri and W. Pinuri, "Data Quality Management Maturity: Case Study National Narcotics Board," 2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), 2021, pp. 206-212, doi: 10.1109/COMNETSAT53002.2021.9530824.
https://ai.stanford.edu/~amaas/data/sentiment/
https://www.kaggle.com/rudrasing/andrew-mass-dataset
B. Wang and C. J. Kuo, "SBERT-WK: A Sentence Embedding Method by Dissecting BERT-Based Word Models," in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 28, pp. 2146-2157, 2020, doi: 10.1109/TASLP.2020.3008390.
https://cran.r-project.org/web/packages/wordpiece/vignettes/basic_usage.html.
Z. Zhang, "Improved Adam Optimizer for Deep Neural Networks," 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), 2018, pp. 1-2, doi: 10.1109/IWQoS.2018.8624183.
Z. Zhang, "Improved Adam Optimizer for Deep Neural Networks," 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), 2018, pp. 1-2, doi: 10.1109/IWQoS.2018.8624183.
H. Shehata and T. Khattab, "Energy Detection Spectrum Sensing in Full-Duplex Cognitive Radio: The Practical Case of Rician RSI," in IEEE Transactions on Communications, vol. 67, no. 9, pp. 6544-6555, Sept. 2019, doi: 10.1109/TCOMM.2019.2916069.