Glossary
- Crowdsourced data:
-
The data obtained by a crowdsourcing process, that is, by contributions (spontaneous or solicited) from a large group of people, especially an online community
- Generalized Maximum Coverage Problem (GMCP):
-
is an extension of the classical Budgeted Maximum Coverage Problem. Given a cost budget B and a set of nondisjoint sets of items in E, where each item e i ∈ E is associated with a cost c i and a weight w i , the GMCP asks for selecting a subset of these sets such that the total weight of the items in the union of the chosen sets is maximized and the total cost of these items is lower than B.
- Itinerary (or sightseeing tour or simply tour):
-
is a detailed plan for a tourist journey, listing the PoIs to visit in a temporal sequence, possibly scheduled in the tourist agenda
- Orienteering Problem (OP):
-
given a set of vertices V, where s i is the score assigned to each vertex v i ∈ V, and t...
References
Brilhante I, Macedo JA, Nardini FM, Perego R, Renso C (2014) Tripbuilder: a tool for recommending sightseeing tours. In: de Rijke M, Kenter T, de Vries AP, Zhai C, de Jong F, Radinsky K, Hofmann K, (eds) Advances in information retrieval, Lecture notes in computer science, vol 8416. Springer, Heidelberg, pp 771–774
Brilhante IR, Macedo JA, Nardini FM, Perego R, Renso C (2015) On planning sightseeing tours with TripBuilder. Inf Process Manag 51(2):1–15
Cohen R, Katzir L (2008) The generalized maximum coverage problem. Inf Process Lett 108(1):15–22
De Choudhury M, Feldman M, Amer-Yahia S, Golbandi N, Lempel R, Cong Y (2010) Automatic construction of travel itineraries using social breadcrumbs. In: Proceedings of the HT, ACM, New York, US, pp 35–44
Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G (2014) A survey on algorithmic approaches for solving tourist trip design problems. J Heuristics 20(3):291–328
Gionis A, Lappas T, Pelechrinis K, Terzi E (2014) Customized tour recommendations in urban areas. In: Proceedings of the 7th ACM international conference on web search and data mining, WSDM ‘14. ACM, New York, pp 313–322
Godart JM (1999) Combinatorial optimisation based decision support system for trip planning. In: Information and communication technologies in tourism 1999. Springer, Heidelberg, pp 318–327
Matai R, Mittal ML, Singh S (2010) In: Davendra D (ed) Traveling salesman problem: an overview of applications, formulations, and solution approaches. InTech, London, UK
Souffriau W, Vansteenwegen P, Vertommen J, Berghe GV, Van Oudheusden D (2008) A personalized tourist trip design algorithm for mobile tourist guides. Appl Artif Intell 22(10):964–985
Surowiecki J (2004) The wisdom of crowds: why the many are smarter than the few and how collective wisdom shapes business, economies, societies, and nations. Doubleday, New York
Vansteenwegen P, Souffriau W (2010) Trip planning functionalities: state of the art and future. Inf Technol Tour 12(4):305–315
Vansteenwegen P, Souffriau W, Vanden Berghe G, Van Oudheusden D (2009) A guided local search metaheuristic for the team orienteering problem. Eur J Oper Res 196(1):118–127
Vansteenwegen P, Souffriau W, Berghe GV, Oudheusden DV (2011) The city trip planner: an expert system for tourists. Expert Sys Appl 38(6):6540–6546
Yoon H, Zheng Y, Xie X, Woo W (2012) Social itinerary recommendation from user-generated digital trails. Pers Ubiquit Comput 16(5):469–484
Acknowledgments
This work has been partially supported by SoBigData (GA. 654024) and BASMATI (GA. 723131) H2020 European projects.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media LLC
About this entry
Cite this entry
Brilhante, I.R., Nardini, F.M., Macedo, J.A., Perego, R., Renso, C. (2017). Crowdsourced Social Data for Recommending Tourist Itineraries. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7163-9_110202-1
Download citation
DOI: https://doi.org/10.1007/978-1-4614-7163-9_110202-1
Received:
Accepted:
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-7163-9
Online ISBN: 978-1-4614-7163-9
eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering