By Maria Teresa Linaza, Amaia Agirregoikoa, Ander Garcia, Jose Ignacio Torres (auth.), Prof. Rob Law, Prof. Matthias Fuchs, Prof. Francesco Ricci (eds.)
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Additional resources for Information and Communication Technologies in Tourism 2011: Proceedings of the International Conference in Innsbruck, Austria, January 26–28, 2011
For every item i of the catalogue, it must be decided if it can be recommended to a user c. From the ratings of a group of items D made by c, the system finds the items out of D that are most similar to i (using similarity measure). g. by majority). Preference-based approach. e. data from different databases from different tourism service providers (dynamic packaging). With the preference-based approach, the creation of recommendations is considered a constraint satisfaction problem or CSP (Tsang, 1993).
3 System Design Following the former version, CT-Planner2 models a tour advisor who makes tour plans customized for individual tourists. It emulates the following roles of the tour advisor: • • • To propose sample tour plans to the tourist, based on the advisor's knowledge about the tourist's profile To handle the tourist's request about the overall character of his tour plan, as well as his special request to include certain POls in the plan or to remove them from the plan To judge the tourist's preference from his feedback 28 The idea of modelling a tour advisor's role to realize user-friendly interactions is also seen in Garcia et at.
McLean, Virginia, USA. Kishimoto, H. & Mizuno, S. (1997). Development of Tour Planning Support System using MOL and Genetic Algorithm. Proceedings from the AI symposium '97. SIG-KBS (in Japanese), Tokyo, Japan. Kurata, Y. (2009). Challenges in User-Adaptive Tour Planning Systems. Proceedings from the AGILE 2009 Workshop on Adaptation in Spatial Communication, Hannover, Germany. Kurata, Y. (2010). Interactive Assistance for Tour Planning. Proceedings from the Spatial Cognition 2010. , Okunuki, K.