Point of interest recommendation based on social and linked open data

  • Giuseppe SansonettiEmail author
Original Article


Location-based services (LBSs) are part of our daily lives due to the huge spread of mobile devices. Such services enable us to access relevant and up-to-date information about our current surroundings at any time and everywhere. The adoption of a data-driven semantic layer coexisting with the traditional Web could help further improve LBSs, allowing them to overcome the barriers imposed by closed databases that do not take advantage of the large amount of public data available on the Internet. In this article, we propose a personalized recommender system of points of interest (POIs) located near the user’s current position, which makes use of the gold mine represented by linked open data (LOD). The target user profile is constructed and updated using two differente sources of feedback. The former is obtained by analyzing her activity on social media (i.e., Facebook). The latter is attained by inviting the user to express her interests and preferences as ratings of a sample of selected images representing specific categories of POIs. Experimental tests performed on real users allowed us to verify the good performance in terms of perceived accuracy and normalized discounted cumulative gain (NDCG). Statistical tests also enabled us to verify the significance of all the obtained results.


Location-based services Recommender systems Social media Linked open data 



  1. 1.
    Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge University Press, New YorkCrossRefzbMATHGoogle Scholar
  2. 2.
    Biancalana C, Gasparetti F, Micarelli A, Sansonetti G (2013) Social semantic query expansion. ACM Trans Intell Syst Technol 4(4):60:1–60:43CrossRefGoogle Scholar
  3. 3.
    Ricci F, Rokach L, Shapira B (2015) Recommender systems handbook, 2nd edn. Springer Publishing Company IncorporatedGoogle Scholar
  4. 4.
    Biancalana C, Gasparetti F, Micarelli A, Sansonetti G (2013) An approach to social recommendation for context-aware mobile services. ACM Trans Intell Syst Technol 4(1):10:1–10:31CrossRefGoogle Scholar
  5. 5.
    Heitmann B, Hayes C (2010) Using linked data to build open, collaborative recommender systems. In: Linked Data Meets Artificial Intelligence, Papers from the 2010 AAAI Spring symposium, Technical Report SS-10-07, Stanford, California, USA, March 22–24, 2010. AAAIGoogle Scholar
  6. 6.
    Di Noia T, Ostuni VC (2015) Recommender systems and linked open data. In: Faber W, Paschke A (eds) Reasoning Web. Web logic rules: 11th International Summer School 2015, Berlin, Germany, July 31–August 4, 2015, Tutorial Lectures. Springer International Publishing, Cham, pp 88–113Google Scholar
  7. 7.
    Gasparetti F (2017) Personalization and context-awareness in social local search: state-of-the-art and future research challenges. Pervasive Mob Comput 38:446–473CrossRefGoogle Scholar
  8. 8.
    Zhao S, King I, Lyu MR (2016) A survey of point-of-interest recommendation in location-based social networks. CoRRGoogle Scholar
  9. 9.
    Yang D, Zhang D, Yu Z, Wang Z (2013) A sentiment-enhanced personalized location recommendation system. In: Proceedings of the 24th ACM Conference on Hypertext and Social Media. HT ’13. ACM, New York, pp 119–128Google Scholar
  10. 10.
    Gurini DF, Gasparetti F, Micarelli A, Sansonetti G (2018) Temporal people-to-people recommendation on social networks with sentiment-based matrix factorization. Futur Gener Comput Syst 78:430–439CrossRefGoogle Scholar
  11. 11.
    Yang D, Zhang D, Yu Z, Yu Z (2013) Fine-grained preference-aware location search leveraging crowdsourced digital footprints from LBSNs. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing. UbiComp ’13. ACM, New York, pp 479–488Google Scholar
  12. 12.
    Yang D, Zhang D, Zheng VW, Yu Z (2015) Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs. IEEE Trans Syst Man Cybern: Syst 45(1):129– 142CrossRefGoogle Scholar
  13. 13.
    Sansonetti G, Gurini DF, Gasparetti F, Micarelli A (2017) Dynamic social recommendation. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. ASONAM ’17. ACM, New York, pp 943–947Google Scholar
  14. 14.
    Bizer C, Heath T, Berners-Lee T (2009) Linked data—the story so far. Int J Semantic Web Inf Syst 5(3):1–22CrossRefGoogle Scholar
  15. 15.
    Passant A (2010) Measuring semantic distance on linking data and using it for resources recommendations. In: AAAI Spring Symposium: Linked Data Meets Artificial Intelligence, Palo Alto. AAAI Press, California, pp 93–98Google Scholar
  16. 16.
    Wang Y, Stash N, Aroyo L, Hollink L, Schreiber G (2009) Semantic relations for content-based recommendations. In: Proceedings of the 5th International Conference on Knowledge Capture. K-CAP ’09. ACM, New York, pp 209–210Google Scholar
  17. 17.
    Di Noia T, Mirizzi R, Ostuni VC, Romito D, Zanker M (2012) Linked open data to support content-based recommender systems. In: Proceedings of the 8th International Conference on Semantic Systems. I-SEMANTICS ’12. ACM, New York, pp 1–8Google Scholar
  18. 18.
    Lo Bue A, Wecker AJ, Kuflik T, Machì A, Stock O (2015) Providing personalized cultural heritage information for the smart region—a proposed methodology. In: Cristea AI, Masthoff J, Said A, Tintarev N (eds) Posters, Demos, Late-breaking Results and Workshop Proceedings of the 23rd Conference on User Modeling, Adaptation, and Personalization (UMAP 2015), Dublin, Ireland, June 29–July 3, 2015. Volume 1388 of CEUR Workshop Proceedings,, pp 1–7Google Scholar
  19. 19.
    Cantador I, Bellogin A, Castells P (2008) A multilayer ontology-based hybrid recommendation model. AI Commun 21(2–3):203–210MathSciNetzbMATHGoogle Scholar
  20. 20.
    De Angelis A, Gasparetti F, Micarelli A, Sansonetti G (2017) A social cultural recommender based on linked open data. ACM, New York, pp 329–332Google Scholar
  21. 21.
    Sansonetti G, Gasparetti F, Micarelli A, Cena F, Gena C (2019) Enhancing cultural recommendations through social and linked open data. User Modeling and User-Adapted InteractionGoogle Scholar
  22. 22.
    Zhang T (2004) Solving large scale linear prediction problems using stochastic gradient descent algorithms. In: Proceedings of the 21st International Conference on Machine Learning. ACM, p 116Google Scholar
  23. 23.
    Mikolov T, Sutskever I, Chen K, Corrado G, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13. Curran Associates Inc, pp 3111–3119Google Scholar
  24. 24.
    Le QV, Mikolov T (2014) Distributed representations of sentences and documents. CoRRGoogle Scholar
  25. 25.
    Järvelin K, Kekäläinen J (2002) Cumulated gain-based evaluation of IR techniques. ACM Trans Inf Syst 20(4):422–446CrossRefGoogle Scholar
  26. 26.
    Fogli A, Micarelli A, Sansonetti G (2018) Enhancing itinerary recommendation with linked open data. In: Stephanidis C (ed) HCI International 2018 – Posters’ Extended Abstracts. Springer International Publishing, Cham, pp 32–39Google Scholar
  27. 27.
    Fogli A, Sansonetti G (2019) Exploiting semantics for context-aware itinerary recommendation. Personal and Ubiquitous ComputingGoogle Scholar
  28. 28.
    Bologna C, De Rosa AC, De Vivo A, Gaeta M, Sansonetti G, Viserta V (2013) Personality-based recommendation in e-commerce. In: CEUR Workshop Proceedings. Volume 997 of CEUR Workshop Proceedings, Aachen, CEUR-WS.orgGoogle Scholar
  29. 29.
    Onori M, Micarelli A, Sansonetti G (2016) A comparative analysis of personality-based music recommender systems. In: CEUR Workshop Proceedings. Volume 1680 of CEUR Workshop Proceedings, Aachen,, pp 55–59Google Scholar
  30. 30.
    Arru G, Feltoni Gurini D, Gasparetti F, Micarelli A, Sansonetti G (2013) Signal-based user recommendation on Twitter. In: Proceedings of the 22nd International Conference on World Wide Web. WWW ’13 Companion. ACM, New York, pp 941– 944Google Scholar
  31. 31.
    Caldarelli S, Gurini DF, Micarelli A, Sansonetti G (2016) A signal-based approach to news recommendation. In: CEUR Workshop Proceedings. Volume 1618 of CEUR Workshop Proceedings, Aachen, CEUR-WS.orgGoogle Scholar
  32. 32.
    Gurini DF, Gasparetti F, Micarelli A, Sansonetti G (2013) A sentiment-based approach to Twitter user recommendation. In: CEUR Workshop Proceedings. Volume 1066 of CEUR Workshop Proceedings, Aachen, Germany, CEUR-WS.orgGoogle Scholar
  33. 33.
    Gurini DF, Gasparetti F, Micarelli A, Sansonetti G (2014) iSCUR: interest and sentiment-based community detection for user recommendation on Twitter. In: Dimitrova V, Kuflik T, Chin D, Ricci F, Dolog P, Houben GJ (eds) User modeling, adaptation, and personalization. Springer International Publishing, Cham, pp 314–319Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of EngineeringRoma Tre UniversityRomeItaly

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