Advertisement

Enhancing Itinerary Recommendation with Linked Open Data

  • Alessandro Fogli
  • Alessandro Micarelli
  • Giuseppe SansonettiEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 850)

Abstract

This paper proposes a recommender system that exploits linked open data (LOD) to perform a social context-aware cross-domain recommendation of personalized itineraries integrated with multimedia and textual content. To this aim, the recommendation engine considers the user profile, the context of use, and the features of the points of interest (POIs) extracted from LOD sources. We describe how to extract data and process it to perform hybrid filtering. All recommendations are based on the user’s features extracted implicitly and explicitly. These features are used to apply content-based filtering and collaborative filtering, weighing results based on similar users’ experience. The preliminary results of an experimental evaluation on a sample of 20 real users show the effectiveness of the proposed system not only in terms of perceived accuracy, but also in terms of novelty, non-obviousness, and serendipity.

Keywords

Recommender systems Points of interest Linked open data 

References

  1. 1.
    Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.): Recommender Systems Handbook. Springer, Boston (2011).  https://doi.org/10.1007/978-0-387-85820-3CrossRefzbMATHGoogle Scholar
  2. 2.
    Biancalana, C., Gasparetti, F., Micarelli, A., Sansonetti, G.: An approach to social recommendation for context-aware mobile services. ACM Trans. Intell. Syst. Technol. 4(1), 10:1–10:31 (2013)CrossRefGoogle Scholar
  3. 3.
    D’Agostino, D., Gasparetti, F., Micarelli, A., Sansonetti, G.: A social context-aware recommender of itineraries between relevant points of interest. In: Stephanidis, C. (ed.) HCI 2016, Part II. CCIS, vol. 618, pp. 354–359. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-40542-1_58CrossRefGoogle Scholar
  4. 4.
    Yoon, H., Zheng, Y., Xie, X., Woo, W.: Social itinerary recommendation from user-generated digital trails. Pers. Ubiquit. Comput. 16(5), 469–484 (2012)CrossRefGoogle Scholar
  5. 5.
    D’Aniello, G., Gaeta, A., Gaeta, M., Loia, V., Reformat, M.Z.: Collective awareness in smart city with fuzzy cognitive maps and fuzzy sets. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1554–1561, July 2016Google Scholar
  6. 6.
    De Angelis, A., Gasparetti, F., Micarelli, A., Sansonetti, G.: A social cultural recommender based on linked open data. In: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization. UMAP 20117. ACM, New York, pp. 329–332 (2017)Google Scholar
  7. 7.
    Biancalana, C., Gasparetti, F., Micarelli, A., Sansonetti, G.: Social semantic query expansion. ACM Trans. Intell. Syst. Technol. 4(4), 60:1–60:43 (2013)CrossRefGoogle Scholar
  8. 8.
    Heitmann, B., Hayes, C.: 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, 22–24 March 2010, Palo Alto, California, USA, pp. 76–81. AAAI Press (2010)Google Scholar
  9. 9.
    D’Aniello, G., Gaeta, M., Loia, V., Orciuoli, F., Tomasiello, S.: A dialogue-based approach enhanced with situation awareness and reinforcement learning for ubiquitous access to linked data. In: 2014 International Conference on Intelligent Networking and Collaborative Systems, pp. 249–256, September 2014Google Scholar
  10. 10.
    Biancalana, C., Flamini, A., Gasparetti, F., Micarelli, A., Millevolte, S., Sansonetti, G.: Enhancing traditional local search recommendations with context-awareness. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 335–340. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-22362-4_29CrossRefGoogle Scholar
  11. 11.
    Biancalana, C., Gasparetti, F., Micarelli, A., Miola, A., Sansonetti, G.: Context-aware movie recommendation based on signal processing and machine learning. In: Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation. CAMRa 2011, pp. 5–10. ACM, New York (2011)Google Scholar
  12. 12.
    Bologna, C., De Rosa, A.C., De Vivo, A., Gaeta, M., Sansonetti, G., Viserta, V.: Personality-based recommendation in e-commerce. In: CEUR Workshop Proceedings, vol. 997. CEUR-WS.org, Aachen (2013)Google Scholar
  13. 13.
    Onori, M., Micarelli, A., Sansonetti, G.: A comparative analysis of personality-based music recommender systems. In: CEUR Workshop Proceedings, vol. 1680, pp. 55–59. CEUR-WS.org, Aachen (2016)Google Scholar
  14. 14.
    Arru, G., Gurini, D.F., Gasparetti, F., Micarelli, A., Sansonetti, G.: Signal-based user recommendation on Twitter. In: Proceedings of the 22nd International Conference on World Wide Web, WWW 2013 Companion, pp. 941–944. ACM, New York (2013)Google Scholar
  15. 15.
    Caldarelli, S., Gurini, D.F., Micarelli, A., Sansonetti, G.: A signal-based approach to news recommendation. In: CEUR Workshop Proceedings, vol. 1618. CEUR-WS.org, Aachen (2016)Google Scholar
  16. 16.
    Gurini, D.F., Gasparetti, F., Micarelli, A., Sansonetti, G.: A sentiment-based approach to twitter user recommendation. In: CEUR Workshop Proceedings, vol. 1066. CEUR-WS.org, Aachen (2013)Google Scholar
  17. 17.
    Gurini, D.F., Gasparetti, F., Micarelli, A., Sansonetti, G.: iSCUR: interest and sentiment-based community detection for user recommendation on Twitter. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, G.-J. (eds.) UMAP 2014. LNCS, vol. 8538, pp. 314–319. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-08786-3_27CrossRefGoogle Scholar
  18. 18.
    Gurini, D.F., Gasparetti, F., Micarelli, A., Sansonetti, G.: Temporal people-to-people recommendation on social networks with sentiment-based matrix factorization. Future Gener. Comput. Syst. 78, 430–439 (2018)CrossRefGoogle Scholar
  19. 19.
    Gasparetti, F., Micarelli, A., Sansonetti, G.: Exploiting web browsing activities for user needs identification. In: 2014 International Conference on Computational Science and Computational Intelligence, vol. 2, pp. 86–89. IEEE Computer Society, Los Alamitos, March 2014Google Scholar
  20. 20.
    Bonifacio, A., Biancalana, C., Gasparetti, F., Micarelli, A., Sansonetti, G.: Implicit evaluation of user’s expertise in scientific domains. In: Stephanidis, C. (ed.) HCI 2017. CCIS, vol. 713, pp. 420–427. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-58750-9_58CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Alessandro Fogli
    • 1
  • Alessandro Micarelli
    • 1
  • Giuseppe Sansonetti
    • 1
    Email author
  1. 1.Department of EngineeringRoma Tre UniversityRomeItaly

Personalised recommendations