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
It is based on the interaction of geospatial technologies and Artificial Intelligence algorithms, adding higher-level functionalities to currently available recommender systems and providing users with a greater range of possibilities to identify leisure activities according to their profile and, beyond that, facilitate the planning of the trip and the decision-making process before and during the stay. In the remainder of this paper we proceed as follows: in the next section the main functionalities of the system are described.
2008). Collaborative Assistance with Spatio-temporal Planning Problems. Proceedings from the Spatial Cognition 2006, Bremen, Germany. , Yasumoto, K. & Ito, M. (2005). A personal navigation system with functions to compose tour schedules based on multiple conflicting criteria. IPSJ Digital Courier I: 528-536. Souriau, W. & Vansteenwegen, P. (2010) Tourist Trip Planning Functionalities: State-of-theArt and Future. Proceedings from International Workshop on Web Engineering & Tourism. Vienna, Austria.
Every night), so that recommendations can be made very efficiently when the client is online. • Solution 4: Most research suggests the use of conversational RS using case-based reasoning. As with the example presented by (Ricci, Mirzadeh, & Venturini, 2006), a conversation system finds out user preferences or suggests examples that the user rates. The system then suggests products to the user and uses their feedback to improve the recommendations. One of the advantages of this system is the fact that it does not require much user feedback.