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Developing an intelligent trip recommender system by data mining methods
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Published:
Mar 15, 2016
Updated:
2016-03-15
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Abstract
Internet has a very wide usage in almost every sector. People are continuously looking and searching for information through internet. Narrowing down relevant search results is not a very simple task. Recommender systems are being used in almost every search related area. Tourism domain is one of these sectors. This study proposes an implementation of an expert system framework which can accurately classify users and make predictions about user classifications for recommending tourism related services. Proposed approach predicts clusters for system users and according to these user clusters, trips, hotels and such services can be recommended individually or as a campaign to target user or user groups.
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Keywords:Â Trip recommender, data mining, expert systems
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How to Cite
Uçar, T., & Karahoca, A. (2016). Developing an intelligent trip recommender system by data mining methods. Global Journal of Information Technology: Emerging Technologies, 6(1), 119–127. https://doi.org/10.18844/gjit.v6i1.398
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References
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[2] Ricci, F. Travel Recommender Systems, IEEE Intelligent Systems, 2002, pp 55-57.
[3] Castillo, L., Armengol, E., OnaindÃa, E., Sebastiá, L., Boticario, J. G., RodrÃguez, A., Fernández, S., Arias, J. D. & Borrajo, D. (2008). SAMAP: An user-oriented adaptive system for planning tourist visits, Expert Systems with Applications, 34(2), pp 1318–1332.
[4] Huang, Y. & Bian, L. (2009). A Bayesian network and analytic hierarchy process based personalized recommendations for tourist attractions over the Internet, Expert Systems with Applications, 36 (1), 933–943.
[5] Schiaffino, S. & Amandi, A. (2009). Building an expert travel agent as a software agent, Expert Systems with Applications, 36(2), 1291–1299.
[6] Crespo, Ã. G., Cuadrado, J. L. L., Palacios, R. C., Carrasco, I. G. & Mezcua, B. R. (2011). Sem-Fit: A semantic based expert system to provide recommendations in the tourism domain, Expert Systems with Applications, 38 (10), 13310–13319.
[7] Garcia, I., Sebastia, L. & Onaindia, E. (2011). On the design of individual and group recommender systems for tourism, Expert Systems with Applications, 38(6), 7683–7692.
[8] Hsua, F.-M., Lina, Y.-T. & Hoc, 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.
[9] Lucas, J. P., Luz, N., Moreno, M. N., Anacleto, R. & Figueiredo, A. A. (2013). A hybrid recommendation approach for a tourism system, Expert Systems with Applications, 40(9), 3532–3550.
[10]Yang, W.-S. & Hwang, S.-Y. (2013). iTravel: A recommender system in mobile peer-to-peer environment, Journal of Systems and Software, 86(1), pp 12–20.
[11]Han, J. & Lee, H. Adaptive landmark recommendations for travel planning: Personalizing and clustering landmarks using geo-tagged social media, Pervasive and Mobile Computing, Available online 13 August 2014, doi:10.1016/j.pmcj.2014.08.002.
[12]Chiang, H.-S. & Huang, T.-C. (2015). User-adapted travel planning system for personalized schedule recommendation, Information Fusion, 21, pp 3-17.
[13]Xu, Z., Chen, L. & Chen, G. Topic based context-aware travel recommendation method exploiting geotagged photos, Neurocomputing, Available online 2 January 2015, doi:10.1016/j.neucom.2014.12.043
[14]Pelleg, D., & Moore, A. (2000). X-means: Extending K-means with efficient estimation of the number of clusters. In Proceedings of the 17th international conference on machine learning (pp. 727–734). Morgan Kaufmann.
[15]Dunn, J. C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3, 32–57.
[16]Bezdek, J. C. (1981). Pattern recognition with fuzzy objective function algorithms. New York: Plenum Press.
[17]Takagi T. & Sugeno, M. (1985). Fuzzy identification of systems and its application to modelling and control. IEEE Trans. On Systems, Man & Cybernetics, 15(1), 116-132
[18]Sugeno M. & Kang G. T. (1988). Structure identification of fuzzy model. Fuzzy Sets and Systems 28(1),15-33
[19]Jang, J.S. (1992). Self-learning fuzzy controllers based on temporal back propagation. IEEE Trans Neural Networks, 3(5), 714-723
[20]Witten, I.H. & Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Publishers, San Francisco.