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
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
References
Fodness, D., Murray, B.: Tourist information search. Ann. Tourism Res. 24, 503–523 (1997)
Beldona, S.: Cohort analysis of online travel information search behavior: 1995–2000. J. Travel Res. 44, 135–142 (2005)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Shapira, B., Rokach, L., Freilikhman, S.: Facebook single and cross domain data for recommendation systems. User Model. User-Adap. Inter. 23, 211–247 (2013)
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)
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)
Jelassi, M.T., Foroughi, A.: Negotiation support systems: An overview of design issues and existing software. Decis. Support Syst. 5, 167–181 (1989)
Marsden, P.V., Campbell, K.E.: Measuring tie strength. Soc. forces 63, 482–501 (1984)
Nelson, R.E.: The strength of strong ties: Social networks and intergroup conflict in organizations. Acad. Manag. J. 32, 377–401 (1989)
Granovetter, M.S.: The strength of weak ties. Am. J. Sociol. 78, 1360–1380 (1973)
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)
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)
Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1, 215–239 (1979)
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)
Theodorson, G.A.: The relationship between leadership and popularity roles in small groups. Am. Sociol. Rev. 22, 58–67 (1957)
Newman, M.E.J.: Analysis of weighted networks. Phys. Rev. E 70, 056131 (2004)
Opsahl, T., Agneessens, F., Skvoretz, J.: Node centrality in weighted networks: Generalizing degree and shortest paths. Soc. Netw. 32, 245–251 (2010)
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)
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)
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)
Langville, A.N., Meyer, C.D.: Deeper inside pagerank. Internet Math. 1, 335–380 (2004)
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)
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
Corresponding author
Editor information
Editors and Affiliations
Rights 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)