Abstract
Typically, people would visit travel websites such as tripadvisor.com, mafengwo.cn, or ctrip.com when planning for their next trip. The lowest airfare, the best hotels, and great attractions can be found on these websites based on requirements provided by users. Millions of traveler reviews, photos, and maps, are also available. With all this information, it may still be time-consuming for users to work out a travel plan, which involves section of attractions from a huge candidate list, and more importantly, an itinerary that guides their daily activities. We therefore proposed a recommendation technique that facilitates the creation of personalized travel plans. Using a tag-based map** algorithm, we create a list of candidate attractions that best match with the user favorite spots. An itinerary containing attractions that are most appealing to users will be derived from the candidate list and we refer to this kind of itinerary as MAI. Meanwhile, by applying K-Means clustering to the list of candidate attractions according to their geographical location, we will be able to produce the shortest itinerary (SI) and the itinerary with the highest performance/price ratio (MEI). A series of experiments have been carried out to help evaluation of our recommendation technique and the results demonstrate that our personalized recommender for travel planning can provide a better and more detailed travel plan that satisfies users with various requirements.
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
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
The first site of each cluster is any site that the user specifies as her favorite site.
- 7.
http://www.mafengwo.cn/mdd/cityroute/10065_5934.html, retrieved at 20:36 on 2018/3/20.
References
Cai, G., Lee, K., Lee, I.: Itinerary recommender system with semantic trajectory pattern mining from geo-tagged photos. Expert Syst. Appl. 94, 32–40 (2018)
Durao, F., Dolog, P.: A personalized tag-based recommendation in social web systems. In: Proceedings of the Workshop on Adaptation and Personalization for Web 2.0, Trento, Italy, pp. 40–49 (2009)
Jain, A., Murty, M., Flynn, P.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)
Kalogerakis, E., Vesselova, O., Hays, J., Efros, A., Hertzmann, A.: Image sequence geolocation with human travel priors. In: Proceedings of the IEEE International Conference on Computer Vision, Tokyo, Japan, pp. 253–260 (2009)
Kurashima, T., Iwata, T., Irie, G., Fujimura, K.: Travel route recommendation using geotags in photos sharing sites. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, Toronto, ON, Canada, pp. 579–588 (2010)
Lee, I., Cai, G., Lee, K.: Mining points-of-interest association rules from geo-tagged photos. In: Proceedings of the 46th Hawaii International Conference on System Sciences, Grand Wailea, Maui, Hawaii, USA, pp. 1580–1588 (2013)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, no. 1, pp. 281–297 (1967)
Marlow, C., Naaman, M., Boyd, D., Davis, M.: Position paper, tagging, taxonomy, flickr, article, to read. In: Collaborative Web Tagging Workshop at WWW2006, Edinburgh, Scotland (2006)
Nakamoto, R., Nakajima, S., Miyazaki, J., Uemura, S.: Tag-based contextual collaborative filtering. IAENG Int. J. Comput. Sci. 34(2), 214–219 (2007)
Robusto, C.: The Cosine-Haversine formula. Am. Math. Mon. 64(1), 38–40 (1957)
Shi, Y., Serdyukov, P., Hanjalic, A., Larson, M.: Personalized landmark recommendation based on geotags from photo sharing sites. In: Proceedings of the 5th International Conference on Weblogs and Social Media, Barcelona, Catalonia, Spain, pp. 622–625 (2011)
Singh, A., Yadav, A., Rana, A.: K-means with three different distance metrics. Int. J. Comput. Appl. 67(10), 13–17 (2013)
Vu, H.Q., Li, G., Law, R., Ye, B.H.: Exploring the travel behaviors of inbound tourists to hong kong using geotagged photos. Tour. Manag. 46, 222–232 (2015)
Yu, Z., Xu, H., Yang, Z., Guo, B.: Personalized travel package with multi-point-of interest recommendation based on crowdsourced user footprints. IEEE Trans. Hum. Mach. Syst. 46, 151–158 (2016)
Kaufman, L., Peter, J.R.: Finding Groups in Data: An Introduction to Cluster Analysis. Probability and Statistics, pp. 253–279. Wiley, Hoboken (1990)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Gu, Y., Zhou, J., Feng, H., Chen, A., Liu, S. (2018). A Recommender for Personalized Travel Itineraries. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11068. Springer, Cham. https://doi.org/10.1007/978-3-030-00021-9_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-00021-9_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00020-2
Online ISBN: 978-3-030-00021-9
eBook Packages: Computer ScienceComputer Science (R0)