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A Ranking Based Combined Approach for Recommending Travel Packages


N. R. Rejin Paul, G. Karpagam, R. Sindhuja, S. Yamini
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The online travel information imposes an increasing challenge for tourists who have to choose from a large number of available travel packages for satisfying their personalized needs. To that end, in this system, there is a need to first analyze the characteristics of the existing travel packages and develop a tourist-area-season topic (TAST) model. This TAST model can represent travel packages and tourists by different topic distributions, where the topic extraction is conditioned on both the tourists and the intrinsic features (i.e., locations, travel seasons) of the landscapes. Then, based on this topic model representation, we propose a cocktail approach to generate the lists for personalized travel package recommendation. Furthermore, we extend the TAST model to the tourist-relation-area-season topic (TRAST) model for capturing the latent relationships among the tourists in each travel group. Finally, we evaluate the TAST model, the TRAST model, and the cocktail recommendation approach on the real-world travel package data. Experimental results show that the TAST model can effectively capture the unique characteristics of the travel data and the cocktail approach is, thus, much more effective than traditional recommendation techniques for travel package recommendation. Also, by considering tourist relationships, the TRAST model can be used as an effective assessment for travel group formation.

N. R. Rejin Paul, G. Karpagam, R. Sindhuja, S. Yamini

Tast,Trast,Cocktail, Ranking

  1. G.D. Abowd et al., "Cyber-Guide: A Mobile Context-Aware Tour Guide," Wireless Networks, vol. 3, no. 5, pp. 421-433, 1997.
  2. G. Adomavicius and A. Tuzhilin, "Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions," IEEE Trans. Knowledge and Data Eng., vol. 17, no. 6, pp. 734-749, June 2005.
  3. D. Agarwal and B. Chen, "fLDA: Matrix Factorization through Latent Dirichlet Allocation," Proc. Third ACM Int’l Conf. Web Search and Data Mining (WSDM ’10), pp. 91-100, 2010.
  4. O. Averjanova, F. Ricci, and Q.N. Nguyen, "Map-Based Interaction with a Conversational Mobile Recommender System," Proc. Second Int’l Conf. Mobile Ubiquitous Computing, Systems, Services and Technologies (UBICOMM ’08), pp. 212-218, 2008.
  5. D.M. Blei, Y.N. Andrew, and I.J. Michael, "Latent Dirichlet Allocation," J. Machine Learning Research, vol. 3, pp. 993-1022, 2003.
  6. R. Burke, "Hybrid Web Recommender Systems," The Adaptive Web, vol. 4321, pp. 377-408, 2007.
  7. B.D. Carolis, N. Novielli, V.L. Plantamura, and E. Gentile, "Generating Comparative Descriptions of Places of Interest in the Tourism Domain," Proc. Third ACM Conf. Recommender Systems (RecSys ’09), pp. 277-280, 2009.
  8. F. Cena et al., "Integrating Heterogeneous Adaptation Techniques to Build a Flexible and Usable Mobile Tourist Guide," AI Comm., vol. 19, no. 4, pp. 369-384, 2006.
  9. W. Chen, J.C. Chu, J. Luan, H. Bai, Y. Wang, and E.Y. Chang, "Collaborative Filtering for Orkut Communities: Discovery of User Latent Behavior," Proc. ACM 18th Int’l Conf. World Wide Web (WWW ’09), pp. 681-690, 2009.
  10. N.A.C. Cressie, Statistics for Spatial Data. Wiley and Sons, 1991.
  11. J. Delgado and R. Davidson, "Knowledge Bases and User Profiling in Travel and Hospitality Recommender Systems" Proc. ENTER 2002 Conf. (ENTER ’02), pp. 1-16, 2002.
  12. U.M. Fayyad and K.B. Irani, "Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning," Proc. Int’l Joint Conf. Artificial Intelligence (IJCAI), pp. 1022-1027, 1993.
  13. F. Fouss et al., "Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation," IEEE Trans. Knowledge and Data Eng., vol. 19, no. 3, pp. 355-369, Mar. 2007.
  14. Y. Ge et al., "Cost-Aware Travel Tour Recommendation," Proc. 17th ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (SIGKDD ’11), pp. 983-991, 2011.
  15. Y. Ge et al., "An Energy-Efficient Mobile Recommender System," Proc. 16th ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (SIGKDD ’10), pp. 899-908, 2010.
  16. M. Gori and A. Pucci, "ItemRank: A Random-Walk Based Scoring Algorithm for Recommender Engines," Proc. 20th Int’l Joint Conf. Artificial Intelligence (IJCAI ’07), pp. 2766-2771, 2007.
  17. U. Gretzel, "Intelligent Systems in Tourism: A Social Science Perspective," Annals of Tourism Research, vol. 38, no. 3, pp. 757-779, 2011.
  18. T.L. Griffiths and M. Steyvers, "Finding Scientific Topics," Proc. Nat’l Academy of Sciences USA, vol. 101, pp. 5228-5235, 2004.
  19. Q. Hao et al., "Equip Tourists with Knowledge Mined from Travelogues," Proc. 19th Int’l Conf. World Wide Web (WWW ’10), pp. 401-410, 2010.
  20. J. Herlocker, J. Konstan, L. Terveen, and J. Riedl, "Evaluating Collaborative Filtering Recommender Systems," ACM Trans. Information Systems, vol. 22, no. 1, pp. 5-53, 2004.

Publication Details

Published in : Volume 1 | Issue 2 | March-April - 2015
Date of Publication Print ISSN Online ISSN
2015-04-25 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
460-466 IJSRSET1522133   Technoscience Academy

Cite This Article

N. R. Rejin Paul, G. Karpagam, R. Sindhuja, S. Yamini , "A Ranking Based Combined Approach for Recommending Travel Packages", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 1, Issue 2, pp.460-466, March-April-2015.
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