Semantic Recommender System for Touristic Context Based on Linked Data

  • Luis Cabrera RiveraEmail author
  • Luis M. Vilches-Blázquez
  • Miguel Torres-Ruiz
  • Marco Antonio Moreno Ibarra
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


The lack of personalization presented in touristic itineraries that are offered by travel agencies involve a little flexibility. Basically, they are designed with the points of interest (POIs) that have more relevance in the area. On the other hand, there are POIs that have agreements with the agencies, which originate a excluding POIs that could be interesting for the tourist. In this work, a method capable to use the user preferences, like POIs and activities that user wants to realize during their vacations is proposed. Moreover, some weighted features such as the max distance that user wants to walk between POIs, and opinions of other users, coming from the web 2.0 by means of social media are taken into account. As result, a personalized route, which is composed of recommended POIs for the user and satisfied the user profile is provided.


Recommender System User Preference User Profile Spatial Database Collaborative Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was partially sponsored by the IPN, CONACYT and SIP, under grant 20140545. Additionally, we are thankful to the reviewers for their invaluable and constructive feedback that helped improve the quality of the paper.


  1. 1.
    Schiaffino S, Amandi A (2009) Intelligent user profiling. In: Artificial intelligence an international perspective, Springer, Berlin, pp 193–216Google Scholar
  2. 2.
    Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl Based Syst 46:109–132Google Scholar
  3. 3.
    Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web. ACM, pp 285–295Google Scholar
  4. 4.
    Zibuschka J, Rannenberg K, Kölsch T (2011) Location-based services. In: Digital privacy, Springer, Berlin, pp 679–695Google Scholar
  5. 5.
    Ruotsalo T, Haav K, Stoyanov A, Roche S, Fani E, Deliai R, Mäkelä E, Kauppinen T, Hyvönen E (2013) SMARTMUSEUM: a mobile recommender system for the web of data. Web Semant Sci Serv Agents World Wide Web 20:50–67Google Scholar
  6. 6.
    García A, Chamizo J, Rivera I, Mencke M, Colomo R, Gómez JM (2009) SPETA: social pervasive e-tourism advisor. Telematics Inform 26(3):306–315Google Scholar
  7. 7.
    Moreno A, Valls A, Isern D, Marin L, Borràs J (2013) SigTur/E-Destination: ontology-based personalized recommendation of tourism and leisure activities. Eng Appl Artif Intell 26(1):633–651Google Scholar
  8. 8.
    Becker C, Bizer C (2008) DBpedia mobile: a location-enabled linked data browser, vol 369. LDOW, BeijingGoogle Scholar
  9. 9.
    Mao Y, Peifeng Y, Wang-Chien L (2010) Location recommendation for location-based social networks. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems (GIS 2010), New York, NY, pp 458–461Google Scholar
  10. 10.
    Middleton SE, Roure DD, Shadbolt NR (2009) Ontology-based recommender systems. In: Staab S, Studer R (eds) Handbook on ontologies, international handbooks information system. Springer, Berlin, pp 779–796Google Scholar
  11. 11.
    Gruber T (1995) Towards principles for the design of ontologies used for knowledge sharing. Int J Hum-Comput Stud 43(5/6):907–928Google Scholar
  12. 12.
    Golemati M, Katifori A, Vassilakis C, Lepouras G, Halatsis C (2007) Creating an ontology for the user profile: method and applications, In: First IEEE international conference on research challenges in information science (RCIS), MoroccoGoogle Scholar
  13. 13.
    Luna V et al (2014) An ontology-based approach for representing the interaction process between user profile and its context for collaborative learning environments. Comput Hum Behav 21:623–643Google Scholar
  14. 14.
    Buriano L, Marchetti M, Carmagnola F, Cena F, Gena C, Torre I (2006) The role of ontologies in context-aware recommender systems. In: 7th international conference on mobile data management, MDM 2006, pp 80, 10–12 May 2006Google Scholar
  15. 15.
    Rich E (1983) Users are individuals: individualizing user models. Int J Man-Mach Stud 18(3):199–214Google Scholar
  16. 16.
    Sarwar BM, Karypis G, Konstan J, Riedl J (2002) Recommender systems for large-scale e-commerce: scalable neighborhood formation using clustering. In Proceedings of the fifth international conference on computer and information technology, vol 1, pp 5–32Google Scholar
  17. 17.
    Linden G, Smith B, York J, (2003) recommendations: item-to-item collaborative filtering, IEEE Internet Comput 7(1):76–80Google Scholar
  18. 18.
    Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749Google Scholar
  19. 19.
    Candillier L, Meyer F, Boullé M (2007) Comparing state-of-the-art collaborative filtering systems. Lect Notes Comput Sci 4571:548–562Google Scholar
  20. 20.
    Schafer JB, Frankowski D, Herlocker J, Sen S (2007) Collaborative filltering recommender systems. Adapt Web 9:291–324Google Scholar
  21. 21.
    Vilches-Blázquez LM (2011) Metodología para la integración basada en ontologías de información de bases de datos heterogéneas en el dominio hidrográfico (Ph.D. thesis Universidad Politécnica de Madrid)Google Scholar
  22. 22.
    Fonseca F, Câmara G, Monteiro AM (2006) A framework for measuring the interoperability of geo-ontologies. Spat Cogn Comput 6(4):307–329Google Scholar
  23. 23.
    Ressler J, Dean M (2007) Geospatial ontology trade study. In: Ontology for the Intelligence Community (OIC-2007), November 28–29, Columbia, MarylandGoogle Scholar
  24. 24.
    Höepken W, Clissmann H (2006) Harmo-TEN tourism harmonisation trans-European network, vol 3. Retrieved from
  25. 25.
    Huang Y, Bian L (2009) A Bayesian network and analytic hierarchy process based personalized recommendations for tourist attractions over the internet. Expert Syst Appl 36(1):933–943Google Scholar
  26. 26.
    Allocca C, D’Aquin M, Motta E (2009) DOOR—towards a formalization of ontology relations. In Dietz JLG (ed) KEOD, pp 13–20Google Scholar
  27. 27.
    Suárez MC, Gómez A (2012) The NeOn methodology for ontology engineering. In: Ontology engineering in a networked world, Springer, Berlin, pp 9–34Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Luis Cabrera Rivera
    • 1
    Email author
  • Luis M. Vilches-Blázquez
    • 2
  • Miguel Torres-Ruiz
    • 1
  • Marco Antonio Moreno Ibarra
    • 1
  1. 1.Centro de Investigación En ComputaciónInstituto Politécnico Nacional UPALM-ZacatencoMexicoMexico
  2. 2.National University of ColombiaBogota D.CColombia

Personalised recommendations