Planning for tourism routes using social networks

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• 作者：Isabel Cenamor ; ^{icenamor@inf.uc3m.es" class="auth_mail" title="E-mail the corresponding author} ; Tomá ; s de la Rosa ^{trosa@inf.uc3m.es" class="auth_mail" title="E-mail the corresponding author} ; Sergio Nú ; ñ ; ez ^{sncovarr@pa.uc3m.es" class="auth_mail" title="E-mail the corresponding author} ; Daniel Borrajo ^{dborrajo@ia.uc3m.es" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Tourism routes ; Automated planning ; Recommender systems
• 刊名：Expert Systems with Applications
• 出版年：2017
• 出版时间：1 March 2017
• 年：2017
• 卷：69
• 期：Complete
• 页码：1-9
• 全文大小：1312 K

文摘

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 the class="smallcaps">PlanTour, a system that creates personalized tourist plans using the human-generated information gathered from the class="smallcaps">minube¹ 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 from class="smallcaps">minube, 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, the class="smallcaps">PlanTour 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.