Skip to main content
SpringerLink
Log in

Pre-trip Ratings and Social Networks User Behaviors for Recommendations in Touristic Web Portals

  • Conference paper
Web Information Systems and Technologies (WEBIST 2015)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 246))

Abstract

Decision-making activities in planning a city visit typically include a pre–visit hunt for information. Hence, users spend the most of the time consulting web portals in the pre–trip phase. The possibility of obtaining social media data and providing user-generated content are powerful tools for help users in the decision process. In this work, we present our framework for profiling both single users and group of users that relies on a not intrusive analysis of the users’ behaviors on social networks/media. Moreover, the analysis of the behavior of small close groups on social networks may help an automatic system in the merge of the different preferences the users may have, simulating somehow a decision process similar to a natural interaction. Such data can be used to provide POI filtering techniques on city touristic portals.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 34.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 44.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    www.tripomatic.com, www.gogobot.com, www.stay.com.

References

  1. Fodness, D., Murray, B.: Tourist information search. Ann. Tourism Res. 24, 503–523 (1997)

    Article  Google Scholar 

  2. Beldona, S.: Cohort analysis of online travel information search behavior: 1995–2000. J. Travel Res. 44, 135–142 (2005)

    Article  Google Scholar 

  3. Wang, Y., Chan, S.C.F., Ngai, G., Leong, H.-V.: Quantifying reviewer credibility in online tourism. In: Decker, H., Lhotská, L., Link, S., Basl, J., Tjoa, A.M. (eds.) DEXA 2013, Part I. LNCS, vol. 8055, pp. 381–395. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  4. Guy, I., Zwerdling, N., Carmel, D., Ronen, I., Uziel, E., Yogev, S., Ofek-Koifman, S.: Personalized recommendation of social software items based on social relations. In: Proceedings of the third ACM conference on Recommender systems, pp. 53–60. ACM (2009)

    Google Scholar 

  5. Said, A., De Luca, E.W., Albayrak, S.: How social relationships affect user similarities. In: Proceedings of the 2010 Workshop on Social Recommender Systems, pp. 1–4 (2010)

    Google Scholar 

  6. Souffriau, W., Vansteenwegen, P.: Tourist trip planning functionalities: State–of–the–art and future. In: Daniel, F., Facca, F.M. (eds.) ICWE 2010. LNCS, vol. 6385, pp. 474–485. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  7. Ardissono, L., Goy, A., Petrone, G., Segnan, M., Torasso, P.: Intrigue: Personalized recommendation of tourist attractions for desktop and handset devices. Appl. Artif. Intell. 17, 687–714 (2003)

    Article  Google Scholar 

  8. McCarthy, K., McGinty, L., Smyth, B., Salamó, M.: The needs of the many: A case-based group recommender system. In: Roth-Berghofer, T.R., Göker, M.H., Güvenir, H.A. (eds.) ECCBR 2006. LNCS (LNAI), vol. 4106, pp. 196–210. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  9. Jameson, A.: More than the sum of its members: Challenges for group recommender systems. In: Proceedings of the Working Conference on Advanced Visual Interfaces. AVI 2004, pp. 48–54. ACM (2004)

    Google Scholar 

  10. Kuflik, T., Stock, O., Zancanaro, M., Gorfinkel, A., Jbara, S., Kats, S., Sheidin, J., Kashtan, N.: A visitor’s guide in an active museum: Presentations, communications, and reflection. J. Comput. Cult. Herit. 3, 1–25 (2011)

    Article  Google Scholar 

  11. Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  12. Yildirim, H., Krishnamoorthy, M.S.: A random walk method for alleviating the sparsity problem in collaborative filtering. In: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 131–138. ACM (2008)

    Google Scholar 

  13. Huang, C.B., Gong, S.J.: Employing rough set theory to alleviate the sparsity issue in recommender system. In: International Conference on Machine Learning and Cybernetics. vol. 3, pp. 1610–1614. IEEE (2008)

    Google Scholar 

  14. Sahebi, S., Cohen, W.W.: Community-based recommendations: A solution to the cold start problem. In: Workshop on Recommender Systems and the Social Web, RSWEB (2011)

    Google Scholar 

  15. Rashid, A.M., Karypis, G., Riedl, J.: Learning preferences of new users in recommender systems: an information theoretic approach. ACM SIGKDD Explor. Newslett. 10, 90–100 (2008)

    Article  Google Scholar 

  16. Shapira, B., Rokach, L., Freilikhman, S.: Facebook single and cross domain data for recommendation systems. User Model. User-Adap. Inter. 23, 211–247 (2013)

    Article  Google Scholar 

  17. Lee, M.-J., Chung, C.-W.: A user similarity calculation based on the location for social network services. In: Yu, J.X., Kim, M.H., Unland, R. (eds.) DASFAA 2011, Part I. LNCS, vol. 6587, pp. 38–52. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  18. Gartrell, M., Xing, X., Lv, Q., Beach, A., Han, R., Mishra, S., Seada, K.: Enhancing group recommendation by incorporating social relationship interactions. In: Proceedings of the 16th ACM International Conference on Supporting Group Work, GROUP 2010, pp. 97–106. ACM (2010)

    Google Scholar 

  19. Jelassi, M.T., Foroughi, A.: Negotiation support systems: An overview of design issues and existing software. Decis. Support Syst. 5, 167–181 (1989)

    Article  Google Scholar 

  20. Marsden, P.V., Campbell, K.E.: Measuring tie strength. Soc. forces 63, 482–501 (1984)

    Article  Google Scholar 

  21. Nelson, R.E.: The strength of strong ties: Social networks and intergroup conflict in organizations. Acad. Manag. J. 32, 377–401 (1989)

    Article  Google Scholar 

  22. Granovetter, M.S.: The strength of weak ties. Am. J. Sociol. 78, 1360–1380 (1973)

    Article  Google Scholar 

  23. Gilbert, E., Karahalios, K.: Predicting tie strength with social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. CHI 2009, pp. 211–220. ACM, New York (2009)

    Google Scholar 

  24. Wilson, C., Boe, B., Sala, A., Puttaswamy, K.P., Zhao, B.Y.: User interactions in social networks and their implications. In: Proceedings of the 4th ACM European Conference on Computer Systems, EuroSys 2009, pp. 205–218. ACM (2009)

    Google Scholar 

  25. Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1, 215–239 (1979)

    Article  Google Scholar 

  26. Banks, L., Wu, S.: All friends are not created equal: An interaction intensity based approach to privacy in online social networks. Int. Conf. Comput. Sci. Eng. 4, 970–974 (2009)

    Google Scholar 

  27. Theodorson, G.A.: The relationship between leadership and popularity roles in small groups. Am. Sociol. Rev. 22, 58–67 (1957)

    Article  Google Scholar 

  28. Newman, M.E.J.: Analysis of weighted networks. Phys. Rev. E 70, 056131 (2004)

    Article  Google Scholar 

  29. Opsahl, T., Agneessens, F., Skvoretz, J.: Node centrality in weighted networks: Generalizing degree and shortest paths. Soc. Netw. 32, 245–251 (2010)

    Article  Google Scholar 

  30. Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. In: Proceedings of the Seventh International World Wide Web Conference Computer Networks and ISDN Systems, vol. 30, pp. 107–117 (1998)

    Google Scholar 

  31. Caso, A., Rossi, S.: Users ranking in online social networks to support poi selection in small groups. In: Posters, Demos, Late-breaking Results and Workshop Proceedings of the 22nd Conference on User Modeling, Adaptation, and Personalization (UMAP2014). CEUR Workshop Proceedings, CEUR-WS.org, vol. 1181 (2014)

    Google Scholar 

  32. Heidemann, J., Klier, M., Probst, F.: Identifying key users in online social networks: A pagerank based approach. In: Proceedings of the International Conference on Information Systems, ICIS 2010, Association for Information Systems, pp. 1–22 (2010)

    Google Scholar 

  33. Langville, A.N., Meyer, C.D.: Deeper inside pagerank. Internet Math. 1, 335–380 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  34. Rossi, S., Caso, A., Barile, F.: Combining users and items rankings for group decision support. In: Bajo, J., et al. (eds.) Trends in Prac. Appl. of Agents, Multi-Agent Sys. and Sustainability. AISC, vol. 372, pp. 151–158. Springer, Heidelberg (2015)

    Chapter  Google Scholar 

Download references

Acknowledgement

The research leading to these results has received funding from the Italian Ministry of University and Research and EU under the PON OR.C.HE.S.T.R.A. project (ORganization of Cultural HEritage for Smart Tourism and Real-time Accessibility).

Author information

Authors and Affiliations

  1. Dipartimento di Ingegneria Elettrica e Tecnologie Dell’Informazione, Universita’ Degli Studi di Napoli “Federico II”, Napoli, Italy

    Silvia Rossi

  2. Dipartimento di Matematica e Applicazioni, Universita’ Degli Studi di Napoli “Federico II”, Napoli, Italy

    Francesco Barile

  3. Dipartimento di Fisica, Universita’ Degli Studi di Napoli “Federico II”, Napoli, Italy

    Antonio Caso & Alessandra Rossi

Authors
  1. Silvia Rossi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Francesco Barile
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Antonio Caso
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alessandra Rossi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Silvia Rossi .

Editor information

Editors and Affiliations

  1. University of Paris, Paris, France

    Valérie Monfort

  2. RWTH Aachen University, Aachen, Germany

    Karl-Heinz Krempels

  3. Department of Information Systems, University of Agder, Kristiansand, Vest-Agder Fylke, Norway

    Tim A. Majchrzak

  4. Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia

    Žiga Turk

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Rossi, S., Barile, F., Caso, A., Rossi, A. (2016). Pre-trip Ratings and Social Networks User Behaviors for Recommendations in Touristic Web Portals. In: Monfort, V., Krempels, KH., Majchrzak, T.A., Turk, Ž. (eds) Web Information Systems and Technologies. WEBIST 2015. Lecture Notes in Business Information Processing, vol 246. Springer, Cham. https://doi.org/10.1007/978-3-319-30996-5_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-30996-5_15

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30995-8

  • Online ISBN: 978-3-319-30996-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation