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Research Article

A hybrid ensemble learning method for tourist route recommendations based on geo-tagged social networks

ORCID Icon, ORCID Icon, ORCID Icon, &
Pages 2225-2246 | Received 13 Sep 2017, Accepted 27 Mar 2018, Published online: 03 May 2018

References

  • AlBaghdadi, A.J. and Alkoot, F.M., 2005. Bagging KNN classifiers using different expert fusion strategies. In: Proceedings of the International Workshop on pattern recognition in information systems, May, Miami, FL. Setubal: INSTICC, 219–224.
  • Batuwita, R. and Palade, V., 2010. FSVM-CIL: fuzzy support vector machines for class imbalance learning. IEEE Transactions on Fuzzy Systems, 18 (3), 558–571. doi:10.1109/TFUZZ.2010.2042721
  • Campigotto, P., et al., 2017. Personalized and situation-aware multimodal route recommendations: the FAVOUR algorithm. IEEE Transactions on Intelligent Transportation Systems, 18 (1), 92–102. doi:10.1109/TITS.2016.2565643
  • Cenamor, I., et al., 2017. Planning for tourism routes using social networks. Expert Systems with Applications, 69, 1–9. doi:10.1016/j.eswa.2016.10.030
  • Chang, L.H., 2016. Change of tour due to bad weather. Journal of Global Scholars of Marketing Science, 26 (4), 315–317. doi:10.1080/21639159.2016.1207849
  • Chen, J. and Zipf, A., 2017. DeepVGI: deep learning with volunteered geographic information. In: Proceedings of the 26th International Conference on World Wide Web Companion, 3–7 April, Perth, Australia. New York: ACM, 771–772.
  • Chen, N.C., et al., 2017. Comprehensive predictions of tourists’ next visit location based on call detail records using machine learning and deep learning methods. In: Big data (BigData Congress), 2017 IEEE International Congress, 25–30 June, Honolulu, HI. New York: IEEE, 1–6.
  • Cheng, A.J., et al., 2011. Personalized travel recommendation by mining people attributes from community-contributed photos. In: Proceedings of the 19th ACM International Conference on multimedia, 28 November–1 December Scottsdale, AZ. New York, NY: ACM, 83–92.
  • Cortes, C. and Vapnik, V., 1995. Support vector machine. Machine Learning, 20 (3), 273–297. doi:10.1007/BF00994018
  • Cover, T. and Hart, P., 1967. Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13 (1), 21–27. doi:10.1109/TIT.1967.1053964
  • Cui, G., Luo, J., and Wang, X., 2017. Personalized travel route recommendation using collaborative filtering based on GPS trajectories. International Journal of Digital Earth, 11 (3), 1–24.
  • Emerson, P., 2013. The original Borda count and partial voting. Social Choice and Welfare, 40 (2), 353–358. doi:10.1007/s00355-011-0603-9
  • Ettema, D., et al., 2017. Season and weather effects on travel-related mood and travel satisfaction. Frontiers in Psychology, 8, 140. doi:10.3389/fpsyg.2017.00140
  • García-Laencina, P.J., et al., 2009. K nearest neighbours with mutual information for simultaneous classification and missing data imputation. Neurocomputing, 72 (7), 1483–1493. doi:10.1016/j.neucom.2008.11.026
  • García-Palomares, J.C., Gutiérrez, J., and Mínguez, C., 2015. Identification of tourist hot spots based on social networks: a comparative analysis of European metropolises using photo-sharing services and GIS. Applied Geography, 63, 408–417. doi:10.1016/j.apgeog.2015.08.002
  • Guns, R. and Rousseau, R., 2014. Recommending research collaborations using link prediction and random forest classifiers. Scientometrics, 101 (2), 1461–1473. doi:10.1007/s11192-013-1228-9
  • Haixiang, G., et al., 2017. Learning from class-imbalanced data: review of methods and applications. Expert Systems with Applications, 73, 220–239. doi:10.1016/j.eswa.2016.12.035
  • Hsu, F.M., Lin, Y.T., and Ho, T.K., 2012. Design and implementation of an intelligent recommendation system for tourist attractions: the integration of EBM model, Bayesian network and Google Maps. Expert Systems with Applications, 39 (3), 3257–3264. doi:10.1016/j.eswa.2011.09.013
  • Jiang, K., et al., 2013. Learning from contextual information of geo-tagged web photos to rank personalized tourism attractions. Neurocomputing, 119, 17–25. doi:10.1016/j.neucom.2012.02.049
  • Khan, K., et al., 2014. DBSCAN: past, present and future. In: Applications of digital information and web technologies (ICADIWT), 2014 Fifth International Conference, 17–19 February, Bangalore, India. New York: IEEE, 232–238.
  • Koswatte, S., McDougall, K., and Liu, X., 2017. VGI and crowdsourced data credibility analysis using spam email detection techniques. International Journal of Digital Earth,  11 (5), 1–13.
  • Lemmens, A. and Croux, C., 2006. Bagging and boosting classification trees to predict churn. Journal of Marketing Research, 43 (2), 276–286. doi:10.1509/jmkr.43.2.276
  • Li, S., et al., 2016. Geospatial big data handling theory and methods: a review and research challenges. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 119–133. doi:10.1016/j.isprsjprs.2015.10.012
  • Liang, K.H., Krus, D.J., and Webb, J.M., 1995. K-fold crossvalidation in canonical analysis. Multivariate Behavioral Research, 30 (4), 539–545. doi:10.1207/s15327906mbr3004_4
  • Liaw, A. and Wiener, M., 2002. Classification and regression by randomForest. R News, 2 (3), 18–22.
  • Lika, B., Kolomvatsos, K., and Hadjiefthymiades, S., 2014. Facing the cold start problem in recommender systems. Expert Systems with Applications, 41 (4), 2065–2073. doi:10.1016/j.eswa.2013.09.005
  • Liu, Y., et al., 2015. A boosting algorithm for item recommendation with implicit feedback. In: Q. Yang and M. Wooldridge, eds. IJCAI'15 Proceedings of the 24th International Conference on Artificial Intelligence, 25–31 July, Buenos Aires, Argentina. Palo Alto: AAAI Press, 1792–1798.
  • Lu, E.H.C., Fang, S.H., and Tseng, V.S., 2016. Integrating tourist packages and tourist attractions for personalized trip planning based on travel constraints. GeoInformatica, 20 (4), 741–763. doi:10.1007/s10707-016-0262-1
  • Majid, A., et al., 2015. A system for mining interesting tourist locations and travel sequences from public geo-tagged photos. Data & Knowledge Engineering, 95, 66–86. doi:10.1016/j.datak.2014.11.001
  • Parthiban, G., Rajesh, A., and Srivatsa, S.K., 2011. Diagnosis of heart disease for diabetic patients using naive Bayes method. International Journal of Computer Applications, 24 (3), 7–11. doi:10.5120/2933-3887
  • Quinlan, J.R., 1986. Induction of decision trees. Machine Learning, 1 (1), 81–106. doi:10.1007/BF00116251
  • Rodriguez, A. and Laio, A., 2014. Clustering by fast search and find of density peaks. Science, 344 (6191), 1492–1496. doi:10.1126/science.1242072
  • Salton, G., 1971. The SMART retrieval system-experiments in automatic document processing. Upper Saddle River, NJ: Prentice-Hall, Inc.
  • Sarwar, B., et al., 2001. Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on world wide web, 1–5 May 2001 Hong Kong. New York, NY: ACM, 285–295.
  • Schafer, J.B., et al., 2007. Collaborative filtering recommender systems. In: P. Brusilovsky, A. Kobsa, and W. Nejdl, eds. The adaptive web. Lecture Notes in Computer Science. Berlin: Springer-Verlag, 291–324.
  • Schapire, R.E., 1990. The strength of weak learnability. Machine Learning, 5 (2), 197–227. doi:10.1007/BF00116037
  • Schapire, R.E., et al., 2003. The boosting approach to machine learning: an overview. In: D.D. Denison, eds. Nonlinear estimation and classification. New York, NY: Springer New York, 149–171.
  • Shao, H., Zhang, Y., and Li, W., 2017. Extraction and analysis of city’s tourism districts based on social media data. Computers, Environment and Urban Systems, 65, 66–78. doi:10.1016/j.compenvurbsys.2017.04.010
  • Su, S., et al., 2016. Characterizing geographical preferences of international tourists and the local influential factors in China using geo-tagged photos on social media. Applied Geography, 73, 26–37. doi:10.1016/j.apgeog.2016.06.001
  • Subramaniyaswamy, V., et al., 2015. Intelligent travel recommendation system by mining attributes from community contributed photos. Procedia Computer Science, 50, 447–455. doi:10.1016/j.procs.2015.04.014
  • Sun, Y., et al., 2015. Road-based travel recommendation using geo-tagged images. Computers, Environment and Urban Systems, 53, 110–122. doi:10.1016/j.compenvurbsys.2013.07.006
  • Xiang, Z.L., Yu, X.R., and Kang, D.K., 2016. Experimental analysis of naïve Bayes classifier based on an attribute weighting framework with smooth kernel density estimations. Applied Intelligence, 44 (3), 611–620. doi:10.1007/s10489-015-0719-1
  • Xu, Z., Chen, L., and Chen, G., 2015. Topic based context-aware travel recommendation method exploiting geotagged photos. Neurocomputing, 155, 99–107. doi:10.1016/j.neucom.2014.12.043
  • Zheng, V.W., et al., 2010. Collaborative location and activity recommendations with GPS history data. In: Proceedings of the 19th international conference on world wide web, 26–30 April, Raleigh, NC. New York, NY: ACM, 1029–1038.
  • Zheng, Y. and Xie, X., 2010. Learning location correlation from GPS trajectories. In: Eleventh international conference on mobile data management, 23–26 May, Kansas City, MO. New York, NY: IEEE, 27–32.
  • Zhou, X., Xu, C., and Kimmons, B., 2015. Detecting tourism destinations using scalable geospatial analysis based on cloud computing platform. Computers, Environment and Urban Systems, 54, 144–153. doi:10.1016/j.compenvurbsys.2015.07.006

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