Automated Adaptive and Sequential Recommendation of Travel Route

International Journal of Computer Trends and Technology (IJCTT)          
© 2017 by IJCTT Journal
Volume-46 Number-2
Year of Publication : 2017
Authors : Swaroopa V Dugani, Dr. Sunanda Dixit
DOI :  10.14445/22312803/IJCTT-V46P118


Swaroopa V Dugani, Dr. Sunanda Dixit "Automated Adaptive and Sequential Recommendation of Travel Route". International Journal of Computer Trends and Technology (IJCTT) V46(2):90-94, April 2017. ISSN:2231-2803. Published by Seventh Sense Research Group.

Abstract -
Big data has deeply rendered into both research and commercial fields such as health care, business and banking sectors. Automated Adaptive and Sequential Recommendation of Travel Route handovers automated and adaptive travel sequence recommendation from large amount of travel data. Unlike any other travel recommendation methods, this method is not only automated but it is personalized to user’s travel interest and also it is able to recommend a travel sequence rather than individual Points of Interest (POIs). This method has large amount of travel data which includes different places, the distributions of cost, visiting time and visiting season of each topic is mined to bridge the gap between user travel preference and travel routes and we also have topical package space. In order to get extensive impression and much better view points of the user topical package model and user travel route, we have made use of the community contributed photos in addition to travel data.

[1] Shuhui Jiang, Xueming Qian, “Personalized Travel Sequence Recommendation on Multi-Source Big Social Media”, IEEE Transaction on big data, vol 2, no-1, january-march 2016
[2] S. Jiang, X. Qian, J. Shen, Y. Fu, and T. Mei, “Author topic model based collaborative filtering for personalized POI recommendation, ”IEEETrans.Multimedia,vol.17,no.6, pp.907–918,Jun.2015.
[3] Y. Zheng, L. Zhang, Z. Ma, X. Xie, and W. Ma, “Recommending friends and locations based on individual location history,” ACM Trans. Web, vol. 5, no. 1, p. 5, 2011.
[4] Q. Yuan, G. Cong, and A. Sun, “Graph-based point-ofinterest recommendation with geographical and temporal influences,” in Proc. 23rd ACM Int. Conf. Inform. Knowl. Manage., pp. 659–668, 2014.
[5] H. Yin, C. Wang, N. Yu, and L. Zhang, “Trip mining and recommendation from geo-tagged photos,” in Proc. IEEE Int. Conf. Multimedia Expo Workshops, pp. 540–545, 2012.
[6] H. Kori, S. Hattori, T. Tezuka, and K. Tanaka, “Automatic generation of multimedia tour guide from local blogs,” in Proc. 13th Int. Conf. Adv. Multimedia Modeling, pp. 690– 699, 2006.
[7] M. Clements, P. Serdyukov, A. de Vries, and M. Reinders, “Personalised travel recommendation based on location cooccurrence,” arXiv preprint arXiv:1106.5213, 2011.
[8] X. Lu, C. Wang, J. Yang, Y. Pang, and L. Zhang, “Photo2trip: Gen- erating travel routes from geo-tagged photos for trip planning,” in Proc. Int. Conf. Multimedia, pp. 143–152, 2010.
[9] Y. Pang, Q. Hao, Y. Yuan, T. Hu, R. Cai, and L. Zhang, “Summarizing tourist destinations by mining usergenerated travelogues and photos,” Comput. Vis. Image Understanding, vol. 115, no. 3, pp. 352–363, 2011.
[10] The IEEE website. [Online]. Available:
[11] L. Cao, J. Luo, A. Gallagher, X. Jin, J. Han, and T. Huang, “A worldwide tourism recommendation system based on geotagged web photos,” in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. pp. 2274–2277, 2010.

Points of Interest (POIs), Topical Package Space, Community Contributed Photos, User Topical Package, GPS, DFD, BTV.