Abstract
Recommendation Systems help users search large amounts of digital contents and identify more effectively the items—products or services—that are likely to be more attractive or useful. As such, it can be characterized as tools that help people making decisions, i.e., make a choice across a vast set of alternatives. This research work has explored decision-making processes in the wide application domain of online services, specifically, hotel booking. This research work is a combination of collaborative filtering (Item-based) recommendation and knowledge-based recommendation system. In which collaborative filtering recommendation will work for user searching and knowledge-based recommendation will work as default recommendation system. In knowledge-based recommendation system it reads the user profile along with his activity of certain last time period as our main knowledge base where this work define the fact of user’s activity. Then this research work applies sorting and counting algorithm. Contextual data are temporarily stored in the knowledge base as the time user stay logged in. Each login will take an updated contextual database. In searching, using item-based k-nearest neighbor algorithm for prediction by collaborative filtering. This work proposed a new rating system which based on hotels performance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Beel, J., Gipp, B., Langer, S., Breitinger, C.: Research-paper recommender systems: a literature survey 17(4), 305–338 (2016)
Aldhahri, E., Shandilya, V., Shiva, S.: Towards an effective crowdsourcing recommendation system a survey of the state-of-the-art. In: IEEE Symposium on Service-Oriented System Engineering, ISBN: 978-1-4799-8356-8 (2015)
Sebastia, L., Garcia, I., Onaindia, E., Guzman, C.: e-Tourism: a tourist recommendation and planning application. In: 20th IEEE International Conference on Tools with Artificial Intelligence (2008)
Chen, J.-H., Chao, K., Shah, N.: Hybrid recommendation system for tourism. In: IEEE 10th International Conference on e-Business Engineering (2013)
Zhao, X., Ji, K.: Tourism e-commerce recommender system based on web data mining. In: The 8th International Conference on Computer Science & Education (2013)
Elkhelifi, A., Ben Kharrat F., Faiz, R.: Recommendation systems based on online user’s action. In: IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, ISBN: 978-1-5090-0154-5 (2015)
Yifan, Y., Junping, D., Dan, F., Lee, J.M.: Design and implementation of tourism activity recognition and discovery system. In: 12th World Congress on Intelligent Control and Automation (WCICA) (2016)
Zhou, X., Xu, Y., Li, Y., Josang, A., Cox, C.: The state-of-the-art in personalized recommender systems for social networking. Springer Link, Vol. 37, Issue 2, pp 119–132, ISSN- 1573-7462 (2012)
Antonio, D.: Exploring recommender system for decision making in e-tourism. Politecnico Di Milano. http://hdl.handle.net/10589/59342 (2012)
Towle, B., Quinn, C.: Knowledge-based recommender systems using explicit user models. AAAI Technical Report WS-00-04. (2000)
Burke, R.: Knowledge-based recommender systems. Researchgate/publication/2378325 (2000)
Thiengburanathum, P., Cang, S., Yu, H.: Overview of personalized travel recommendation systems. In: 22th International Conference on Automation & Computing, University of Essex, Colchester CO4 3SQ (2016)
Tang, Z., Wen, Z.: Recommendation system based on collaborative filtering in rapid miner. Comput. Model. New Technol. 18(11), 1004–1008 (2014)
Zhang, L., Hu, C., Chen, Q., Chen, Y., Shi, Y.: Domain knowledge based personalized recommendation model and its application in cross-selling. In: Proceedings of the International Conference on Computational Science, ICCS, Vol. 9, pp. 1314–1323. Elsevier (2012)
Burke, R.: Integrating knowledge-based and collaborative-filtering recommender systems. AAAI Technical Report WS-99-01 (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rahman, M.M., Zaki, Z.B.M., Alwi, N.H.B.M., Monirul Islam, M. (2019). A Hybrid Approach to Improve Recommendation System in E-Tourism. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 755. Springer, Singapore. https://doi.org/10.1007/978-981-13-1951-8_70
Download citation
DOI: https://doi.org/10.1007/978-981-13-1951-8_70
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1950-1
Online ISBN: 978-981-13-1951-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)