Smart city: Recommendation of personalized services in patrimony tourism

The changes in urban lifestyles, impose new production processes, research of the creativity, and development of digital technologies. These changes offer a range of services for citizens and for the infrastructure of the city, through diverse platforms through innovation of a new concept “smart city”. The concept offers sustainable solutions for modern cities by the invention of new information and communications technologies. But, in front of the diversification of platforms and the unlimited expansion of information on the web, the user becomes incapable to manage this quantity of information. Specifically, in the tourism sector, the users are surrounded by many services and resources that are offered but unsuitable with the user preferences. Therefore, this information overload affects the consumption of services which makes the integration of the intelligent systems an indispensable operation. This paper presents a personalized architecture of tourist services based on the user profile in the smart city. This architecture is based on the technique of collaborative filtering of the recommendation system.

[1]  D. Asanov.  Algorithms and Methods in Recommender Systems , 2011 .

[2]  Ling Bian,et al.  A Bayesian network and analytic hierarchy process based personalized recommendations for tourist attractions over the Internet , 2009, Expert Syst. Appl..

[3]  Rachida Ajhoun,et al.  New Approach for Service Discovery and Prediction Based on Intentional Perspective and Recommendation , 2015 .

[4]  Sachio Hirokawa,et al.  Discovery of Implicit Feature Words of Place Name , 2015, Tourism Informatics.

[5]  Camille Rosenthal-Sabroux,et al.  Smart City and Value Creation , 2014 .

[6]  Sachio Hirokawa,et al.  Extraction of Tourism Objects from Blogs , 2015, Tourism Informatics.

[7]  Yasuhiko Morimoto,et al.  Recommending Hotels by Social Conditions of Locations , 2015, Tourism Informatics.

[8]  D. Fesenmaier,et al.  Destination Recommendation Systems: Behavioural Foundations and Applications , 2006 .

[9]  Touhid Bhuiyan.  SimTrust : The Algorithm for Similarity-Based Trust Network Generation , 2013 .

[10]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[11]  X Guo,et al.  Personalized government online services with recommendation techniques , 2006 .

[12]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[13]  Robin D. Burke,et al.  Hybrid Web Recommender Systems , 2007, The Adaptive Web.

[14]  Renata Paola Dameri,et al.  Comparing Smart and Digital City: Initiatives and Strategies in Amsterdam and Genoa. Are They Digital and/or Smart? , 2014 .