The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-1/W5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W5, 83–90, 2015
https://doi.org/10.5194/isprsarchives-XL-1-W5-83-2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W5, 83–90, 2015
https://doi.org/10.5194/isprsarchives-XL-1-W5-83-2015

  10 Dec 2015

10 Dec 2015

AN ONTOLOGY-BASED TOURISM RECOMMENDER SYSTEM BASED ON SPREADING ACTIVATION MODEL

Z. Bahramian and R. Ali Abbaspour Z. Bahramian and R. Ali Abbaspour
  • School of Surveying and Spatial Information Engineering, College of Engineering, University of Tehran, North Kargar Ave., After Jalal Al Ahmad Crossing, Tehran, Iran

Keywords: Recommender System, Ontology, Tourism, Personalization, Point of Interest, Spreading Activation

Abstract. A tourist has time and budget limitations; hence, he needs to select points of interest (POIs) optimally. Since the available information about POIs is overloading, it is difficult for a tourist to select the most appreciate ones considering preferences. In this paper, a new travel recommender system is proposed to overcome information overload problem. A recommender system (RS) evaluates the overwhelming number of POIs and provides personalized recommendations to users based on their preferences. A content-based recommendation system is proposed, which uses the information about the user’s preferences and POIs and calculates a degree of similarity between them. It selects POIs, which have highest similarity with the user’s preferences. The proposed content-based recommender system is enhanced using the ontological information about tourism domain to represent both the user profile and the recommendable POIs. The proposed ontology-based recommendation process is performed in three steps including: ontology-based content analyzer, ontology-based profile learner, and ontology-based filtering component. User’s feedback adapts the user’s preferences using Spreading Activation (SA) strategy. It shows the proposed recommender system is effective and improves the overall performance of the traditional content-based recommender systems.