Planning for tourism routes using social networks. (1st March 2017)

• Record Type: Journal Article
• Authors: Cenamor, Isabel; de la Rosa, Tomás; Núñez, Sergio; Borrajo, Daniel
• Is Part Of: Expert systems with applications. Volume 69(2017)
• Page Start: 1
• Page End: 9
• Publication Date: 2017-03-01
• Subjects: Tourism routes -- Automated planning -- Recommender systems; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2016.10.030
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: Traveling recommendation systems have become very popular applications for organizing and planning tourist trips. Among other challenges, these applications are faced with the task of maintaining updated information about popular tourist destinations, as well as providing useful tourist guides that meet the users preferences. In this work we present thePlanTour, a system that creates personalized tourist plans using the human-generated information gathered from theminube 1 traveling social network. The system follows an automated planning approach to generate a multiple-day plan with the most relevant points of interest of the city/region being visited. Particularly, the system collects information of users and points of interest fromminube, groups these points with clustering techniques to split the problem into per-day sub-problems. Then, it uses an off-the-shelf domain-independent automated planner that finds good quality tourist plans. Unlike other tourist recommender systems, thePlanTour planner is able to organize relevant points of interest taking into account user's expected drives, and user scores from a real social network. The paper also highlights how to use human provided recommendations to guide the search for solutions of combinatorial tasks. The resulting intelligent system opens new possibilities of combining human-generated knowledge with efficient automated techniques when solving hard computational tasks. From an engineering perspective we advocate for the use of declarative representations of problem solving tasks that have been shown to improve modeling and maintenance of intelligent systems.