Online Travel Agent for Tourism System Using Big Data and Cloud
Abstract
Nowadays, more and more people can enjoy fast internet access that can be used for various activities such as browsing, shopping online, video calls, playing games and so on. Businesses are also utilizing this very rapid increase in internet technology. They sell products and services through the internet with various attractive offers and competing with each other to increase their sales. One strategy that can be done to get more sales is through the method of personalizing services for customers. The personalization aspect in e-tourism has been predicted to increase. Customers who are making valuable data at every stage of their journey are making a challenge for travel companies to collect and link these data points to improve their customer experience. Learning the customer behaviour can be very significant for Online Travel Agent. Because collecting millions of search results through their services and provide a smart travel experience, Online Travel Agent in Indonesia must use Big Data and Cloud technology alignment to win the competition in the market. The entire data lifecycle must be simple because of the needs of users to keep batch ingesting a lot of data likes once in an hour. Streaming analytics has grown over the past few years, it has become one of the most critical components of most of the businesses. We proposed Online Travel Agent (OTA) for Tourism System Using Big Data and Cloud.
References
[2] Davenport, T. H. 2013. At the Big Data Crossroads: turning towards a smarter travel experience, p. 28, Available at: http://amadeusblog.com/wp-content/uploads/Amadeus-Big-Data-Report.pdf
[3] Ekstrand, M.D., Riedl, J.T. and Konstan, J.A. 2011. Collaborative filtering recommender systems. Foundations and Trends in Human–Computer Interaction, 4(2): 81–173.
[4] Hosseini, M. 2016. Analysis of Travel Behavior Big Data by Hadoop Ecosystem, i: 1–6.
[5] Jensen, B. S., Gallego, J. S. and Larsen, J. 2012. A predictive model of music preference using pairwise comparisons. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1977–1980. IEEE.
[6] Kumar, M. 2017. Google Cloud Platform : A Powerful Big Data Analytics Cloud Platform. Available at: https://www.researchgate.net/publication/313839222_Google_Cloud_Platform_A_Powerful_Big_Data_Analytics_Cloud_Platform
[7] Linden, G. D., Jacobi, J. A. and Benson E. A. 2001. Collaborative recommendations using item-to-item similarity mappings, US Patent 6,266,649.
[8] Linden, G., Smith, B., and York, J. 2003. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet computing, (1), 76-80. Available at: https://www.researchgate.net/publication/3419552_Linden_G_Smith_B_and_York_J_'Amazoncom_recommendations_item-to-item_collaborative_filtering'_Internet_Comput_IEEE_7
[9] Murdaningsih, D. 2018. Indonesia Disarankan Fokus di Empat Industri Prioritas. Republika Online. Available at: https://www.republika.co.id/berita/ekonomi/korporasi/18/07/12/pbow%200h368-indonesia-disarankan-fokus-di-empat-industri-prioritas.
[10] Pan, W., Li C., and Zhong, M. 2018. Personalized recommendation with implicit feedback via learning pairwise preferences over item-sets. Knowledge and Information Systems. Available at: https://www.researchgate.net/publication/322619792_Personalized_recommendation_with_implicit_feedback_via_learning_pairwise_preferences_over_item-sets
[11] Qian, L., Gao, J., and Jagadish, H. V. 2015. Learning user preferences by adaptive pairwise comparison. In Proceedings of the VLDB Endowment, 8(11): 1322–1333. Available at: http://www.vldb.org/pvldb/vol8/p1322-qian.pdf
[12] Rabanser, U., Ricci, F. 2005. Recommender Systems: Do They Have a Viable Business Model in e-Tourism? in: Frew A.J. (eds) Information and Communication Technologies in Tourism. Springer, Vienna.
[13] Resnick, P. et al. 1994. Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the ACM conference on Computer supported cooperative work, pages 175–186. ACM.
[14] Schafer, J. B., Konstan, J. and Riedl, J. 1999. Recommender systems in e-commerce. In Proceedings of the 1st ACM conference on Electronic commerce, pages 158–166. ACM.
[15] Song, H. and Liu, H. 2017. Predicting Tourist Demand Using Big Data. Analytics in Smart Tourism Design: Concepts and Methods, pp. 13 – 29, 2017. Available at: https://www.researchgate.net/publication/313839222_Google_Cloud_Platform_A_Powerful_Big_Data_Analytics_Cloud_Platform
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