Authors:
Pijitra Jomsri,Worasit Choochaiwattana,DOI NO:
https://doi.org/10.26782/jmcms.spl.9/2020.05.00006Keywords:
User profiler,recommender system,travel,recommender system,,Abstract
The tourist information recommendation system is useful for both tourist’s them-selves and tourist operators. This recommendation system can support tourists to spend less time searching for tourist attraction information and also be a channel for public relation to create incentives for tourists to use the services. User profiles is an important part of recommendation system that is responsible for finding the users’ interest and is a good representative for each tourist. However, creating a user profile to suitable each user in the tourist information recommendation system is still considered as challenging due to insufficient data collection. In addition, the use of social networks at present is becoming increasingly popular and is a source of information that has many users which can be extracted to represent the interests of each user. Therefore, this research has studied the recommendations for creating a user profile for the tourism information recommendation system in Thailand by using ATRU model to create User Profiling.Refference:
I. Bennettม J., C. Elkan, B. Liu, P. Smyth, and D. Tikk, “Kdd cup and workshop 2007”,ACM SIGKDD Explorations Newsletter, vol. 9, no. 2, pp. 51–52, 2007
II. Billsus, D.and Pazzani,M. J., “User Modeling for Adaptive News Access. User Modeling and UserAdapted Interaction,” 10, pp. 147-180, 2000.
III. Buhalis, D., Law, R., “Progress in information technology and tourism management: 20 years on and 10 years after the internetthe state of etourism research,” Tourism Management 29(4), 609 – 623 (2008). DOI http://dx.doi.org/10.1016/j.tourman.2008.01.005. URL http://www.sciencedirect.com/science/article/pii/S0261517708000162
IV. Cantador, I. Bellogin, A. and Castells, P., “Ontology-Based Personalized and Context-Aware Recommendations of News Items,” In Proceedings of the 7th International Conference on Web Intelligence, pp. 562-565. IEEE. 2008
V. Gauch, S., Speretta, M., Chandramouli, A., and Micarelli, A., “User profiles for personalized information access,” The adaptive web, pp. 54-89. Springer,2007.
VI. J. S. Breese, D. Heckerman, and C. Kadie, “Empirical analysis of predictive algorithms for collaborative filtering”,The Fourteenth conference on Uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc., pp. 43–52,1998
VII. K. Goldberg, T. Roeder, D. Gupta, and C. Perkins, “Eigentaste: A constant time collaborative filtering algorithm,” information retrieval, vol. 4, no. 2, pp. 133–151, 2001
VIII. Kbaier M. E. B. H., Masri, H. ; KrichenS. ,“A Personalized Hybrid Tourism Recommender System”, 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), 2017
IX. Kim, H. R. and Chan, P. K., “Learning implicit user interest hierarchy for context in personalization,” In Proceedings of the 8th international conference on Intelligent user interfaces, pp. 101–108. ACM, 2003.
X. Liu, J. Dolan, P. and Pedersen, E. R., “Personalized news recommendation based on click behavior,” In Proceedings of the 15th international conference on intelligent user interfaces, pp. 31–40. ACM, 2010.
XI. Moreno, A., Sebastia´, L., Vansteenwegen, P., “Tours’15: Workshop on tourism recommender systems,” the 9th ACM Conference on Recommender Systems, RecSys’15, pp. 355–356. ACM, New York, NY, USA ,2015
XII. Moreno, A., Sebastia´, L., Vansteenwegen, P.: Tours’15: Workshop on tourism recommender systems. In: Proceedings of the 9th ACM Conference on Recommender Systems, RecSys’15, pp. 355–356. ACM, New York, NY, USA (2015). DOI 10.1145/2792838. 2798713. URL http://doi.acm.org/10.1145/2792838.2798713.
XIII. Ricci, F., “Travel recommender systems, “ IEEE Intelligent Systems”, 17(6), 55–57, 2002
XIV. Ricci, F., Werthner, H., “Case base querying for travel planning recommendation,” Information Technology & Tourism 4(3-4), 215–226 ,2001
XV. Singh, S., Shepherd, M., Duffy, J. and Watters, C., “An Adaptive User Profile for Filtering News Based on a User Interest Hierarchy,” In Proceedings of the American Society for Information Science and Technology, Volume 43, Issue 1, pp. 1-21, 2006.