Implementation of Modified Smart travel Advisory System of User Interest By Using Preprocessing
 |
International Journal of Computer Trends and Technology (IJCTT) |  |
| © 2019 by IJCTT Journal | ||
| Volume-67 Issue-10 |
||
| Year of Publication : 2019 | ||
| Authors : Shital Narayansing. Raul, Prof.Dr.Nitin N. Patil | ||
| DOI : 10.14445/22312803/IJCTT-V67I10P111 |
MLA Style:Shital Narayansing. Raul, Prof.Dr.Nitin N. Patil"Implementation of Modified Smart travel Advisory System of User Interest By Using Preprocessing," International Journal of Computer Trends and Technology 67.10 (2019):63-68.
APA Style Shital Narayansing. Raul, Prof.Dr.Nitin N. Patil. Implementation of Modified Smart travel Advisory System of User Interest By Using PreprocessingInternational Journal of Computer Trends and Technology, 67(10),63-68.
Abstract
Recently there is big encroachment in location based techniques are done. It has fundamentally modified social media networking services Which are Related to users to share their location and other information about their location and visiting places such as geo-tagged photos and notes. At every moment different data gets uploaded on location based Social Media (i.e. facebook and flickr) like check-ins, GPS locations, tagging friends, travel routes, shopping, dining and photos all these are unstructured data. The comfort of user convenience has resulted in tremendously increased user count of the Internet. Taking into consideration these all things, our proposed work in this paper is to build an improved travel route advisory and recommendation system. Such a system gives complete freedom to users for choosing their suitable trip options by giving their preference keyword. To recommend personalized Point of interest (POI) sequence, first the popular routes are ranked according to the similarity between user package and route package. Then top ranked routes are further optimized by social similar users travel records.
Reference
[1] Y. Arase, X. Xie, T. Hara, and S. Nishio. "Mining people? strip from large scale geo-tagged photos". In Proceedings of the 18th ACM international conference on Multimedia, pages 133–142. ACM, 2010.
[2] X. Cao, L. Chen, G. Cong, and X. Xiao. Keyword-aware optimal route search. Proceedings of the VLDB Endowment, 5(11):1136–1147, 2012.
[3] H. Yin, B. Cui, Y. Sun, Z. Hu, and L. Chen. LCARS: A spatial item recommender system. ACM Transactions on Information Systems (TOIS), 32(3):11, 2014.
[4] D. Chen, C. S. Ong, and L. Xie. Learning points and routes to recommend trajectories. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pages 2227–2232, 2016.
[5] M. Clements, P. Serdyukov, A. De Vries, and M. Reinders ,Using Flicker Geo Tag To Predict User Travel Behavior, In proceeding of the 33rd International ACMSIGIR Conference Research Development Information Retrieval, 2010
[6] D. Chen, C. S. Ong, and L. Xie,” Learning Points And Routes To Recommend Trajectories.” In Proceedings of the 25th ACM International Conference On Information And Knowledge Management, 2016
[7] B. Zheng, N. J. Yuan, K. Zheng, X. Xie, S. Sadiq , and X. Zhou, “Approximate Keyword Search In Semantic Trajectory Database” In Data Engineering (ICDE), IEEE 31st International Conference,2015.
[8] W. T. Hsu, Y. T. Wen, L. Y. Wei, and W. C. Peng, -Skyline travel routes: Exploring skyline for trip planning,? in Proceed. IEEE 15th Int. Conf. Mobile Data Manage., 2014, pp. 31–36.
[9] H. Liu, T. Mei, J. Lou, H. Li, and S. Li, “Finding perfect rendezvous on the go: accurate mobile visual localization and its applications to routing,” in Proceedings of the 20th ACM international conference on Multimedia. ACM, 2012, pp. 9–18.
[10] [2] J. Li, X. Qian, Y. Y. Tang, L. Yang, and T. Mei, “GPS estimation for places of interest from social users? uploaded photos,” IEEE Transactions on Multimedia, vol. 15, no. 8, pp. 2058–2071, 2013.
[11] S. Jiang, X. Qian, J. Shen, Y. Fu, and T. Mei, “Author topic model based collaborative filtering for personalized poi recommendation, ”IEEE Transactions on Multimedia, vol. 17, no. 6, pp. 907–918,2015.
[12] J. Sang, T. Mei, and C. Sun, J.T. and Xu, “Probabilistic sequential pois recommendation via check-in data,” in Proceedings of ACMSIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 2012.
[13] Y. Zheng, L. Zhang, Z. Ma, X. Xie, and W. Ma, “Recommending friends and locations based on individual location history,” ACM Transactions on the Web, vol. 5, no. 1, p. 5, 2011.
[14] H. Gao, J. Tang, X. Hu, and H. Liu, “Content-aware point of interest recommendation on location-based social networks,” in Proceedings of 29th International Conference on AAAI. AAAI, 2015.
Keywords
Location based technique, check-ins, GPS , POI ,Tags.