Abstract
On-line photo sharing services allow users to share their touristic experiences. Tourists can publish photos of interesting locations or monuments visited, and they can also share comments, annotations, and even the GPS traces of their visits. By analyzing such data, it is possible to turn colorful photos into metadata-rich trajectories through the points of interest present in a city.
In this paper we propose a novel algorithm for the interactive generation of personalized recommendations of touristic places of interest based on the knowledge mined from photo albums and Wikipedia. The distinguishing features of our approach are multiple. First, the underlying recommendation model is built fully automatically in an unsupervised way and it can be easily extended with heterogeneous sources of information. Moreover, recommendations are personalized according to the places previously visited by the user. Finally, such personalized recommendations can be generated very efficiently even on-line from a mobile device.
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References
Backstrom, L., Leskovec, J.: Supervised random walks: predicting and recommending links in social networks. In: WSDM 2011, pp. 635–644 (2011)
Clements, M., Serdyukov, P., de Vries, A.P., Reinders, M.J.T.: Using flickr geotags to predict user travel behaviour. In: SIGIR 2010 (July 2010)
Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Trans. on Patt. Anal. and Mach. Intell. 24, 603–619 (2002)
Crandall, D.J., Backstrom, L., Huttenlocher, D., Kleinberg, J.: Mapping the world’s photos. In: WWW, pp. 761–770. ACM (2009)
De Choudhury, M., Feldman, M., Amer-Yahia, S., Golbandi, N., Lempel, R., Yu, C.: Automatic construction of travel itineraries using social breadcrumbs. In: HT, pp. 35–44 (2010)
Jamali, M., Ester, M.: TrustWalker: a random walk model for combining trust-based and item-based recommendation. In: SIGKDD 2009, pp. 397–406 (2009)
Konstas, I., Stathopoulos, V., Jose, J.M.: On social networks and collaborative recommendation. In: Proceedings SIGIR 2009, vol. 8(3), p. 195 (2009)
Kurashima, T., Iwata, T., Irie, G., Fujimura, K.: Travel route recommendation using geotags in photo sharing sites. In: CIKM, pp. 579–588 (2010)
Lu, X., Wang, C., Yang, J.M., Pang, Y., Zhang, L.: Photo2trip: generating travel routes from geo-tagged photos for trip planning. In: MM, pp. 143–152 (2010)
Rattenbury, T., Good, N., Naaman, M.: Towards automatic extraction of event and place semantics from flickr tags. In: SIGIR 2007, pp. 103–110 (2007)
Tong, H., Faloutsos, C.: Center-piece subgraphs: problem definition and fast solutions. In: KDD, pp. 404–413 (2006)
Yildirim, H., Krishnamoorthy, M.S.: A random walk method for alleviating the sparsity problem in collaborative filtering. In: Proceedings RecSys 2008, p. 131 (2008)
Yin, Z., Cao, L., Han, J., Luo, J., Huang, T.: Diversified trajectory pattern ranking in geo-tagged social media. In: SDM (2011)
Zheng, Y., Zhao, M., Song, Y., Adam, H., Bissacco, U., Brucher, F., Chua, T., Neven, H.: Tour the world: Building a web-scale landmark recognition engine. In: CVPR, pp. 1085–1092 (2009)
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Lucchese, C., Perego, R., Silvestri, F., Vahabi, H., Venturini, R. (2012). How Random Walks Can Help Tourism. In: Baeza-Yates, R., et al. Advances in Information Retrieval. ECIR 2012. Lecture Notes in Computer Science, vol 7224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28997-2_17
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DOI: https://doi.org/10.1007/978-3-642-28997-2_17
Publisher Name: Springer, Berlin, Heidelberg
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