Travel Data Sequence from Multi-Source Recommendation System


  • Ms. Shobarani
  • Dr. Anandam Velagandula
  • Mr. Ravula Arun Kumar
  • B Anandkumar





Geo-tagged photos, Social media, Route planning, GPS trajectories, Place of interest Travel Recommendation, User preferences.


Due to different sort of preferences and restrictions of a trip such as time source limitation and every tourist’s destination points the travel based recommendation has become a challenging task. Most importantly the data generated by the geo-tagged social channel from the geo based tag tweets, snapshots of credentials.  Due to examining this, extended data allows us to invent the profiles, daily mobility patterns, and results of the user’s. To resolve the issues and challenges of capacity providing their personalized and sequential travel to make package recommendation to a topical package model and to take using social media info in which mechanically mine person travel interest with another quality like time, cost, and period of wayfaring. Here, we had a proposal that a travel data sequence after a multi source recommendation system. We implemented a location recommendation system that derives personal preferences while accounting for restraints irremissibly by road capacity in order to change the demand of travel. We first infer unobserved preferences using a machine learning technique from data mining records. It extends our method to provide personalized suggestions based on user geo co-ordinates points. By utilizing the tree based hierarchal graphs (TBHG), location histories of the multiple users’ have been modeled.  In order to collect the selected places interest level and travel knowledge of user’s, the HITS model had developed based on TBHG. Finally, hybrid filtering approach based on HITS is utilized to get the global positioning system (GPS) based personalized recommendation system. And for image based search similar images with the tag information are retrieved for the query image users.



[1] H. Liu, T. Mei, J. Luo, 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.

[2] S. Jiang, X. Qian, J. Shen, Y. Fu, and T. Mei, “Author topic model based collaborative filtering for personalized POI recommendation,†IEEE Trans.Multimedia, vol. 17, no. 6, pp. 907– 918, Jun. 2015.

[3] R. Akcelik. Travel time functions for transport planning purposes: Davidson’s function, its time dependent form and alternative travel time function. Australian Road Research, 21(3), 1991.

[4] L. Alexander, S. Jiang, M. Murga, and M. C. Gonz´alez. Origin–destination trips by purpose and time of day inferred from mobile phone data. Transportation Research Part C: Emerging Technologies, 2015.

[5] M. Clements, P. Serdyukov, A. de Vries, and M. Reinders, ―Using flickr geo-tags to predict user travel behaviour,‖ in Proc. 33rd Int. ACMSIGIR Conf. Res. Develop. Inf. Retrieval, 2010, pp. 851– 852.

[6] H. Feng and X. Qian, ―Mining user-contributed photos for personalized product recommendation,‖ Neurocomputing., vol. 129, pp. 409–420,2014.

[7] Y. Gao, J. Tang, R. Hong, Q. Dai, T. Chua, and R. Jain, ―W2go: A travel guidance system by automatic landmark ranking,‖ in Proc. Int.Conf. Multimedia, 2010, pp. 123–132.

[8] Y. Gao et al. W2go: a travel guidance system by automatic landmark ranking. In ACM MM, 2010.

[9] Q. Hao et al. Equip tourists with knowledge mined from travelogues. In WWW, 2010.

[10] B. Berjani and T. Strufe. A recommendation system for spots in location-based online social networks. In Proceedings of the 4th Workshop on Social Network Systems, page 4. ACM, 2011.

[11] D. M. Blei, A. Y. Ng, and M. I. Jordan, ―Latent Dirichlet allocation,‖J. Mach. Learn. Res., vol. 3, pp. 993–1022, 2003.

[12] A. Cheng, Y. Chen, Y. Huang, W. Hsu, and H. Liao, ―Personalized travel recommendation by mining people attributes from community contributed photos,‖ in Proc. 19th ACM Int. Conf. Multimedia, 2011,pp. 83–92.

[13] Defu Lian, Xing Xie, Vincent W. Zheng, Nicholas Jing Yuan, Fuzheng Zhang, and Enhong Chen. Cepr: A collaborative exploration and periodically returning model for location prediction. ACM Trans. Intell. Syst. Technol., 6(1):8:1–8:27, April 2015.

[14] Defu Lian, Cong Zhao, Xing Xie, Guangzhong Sun, Enhong Chen, and Yong Rui. GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation. In Proceedings of KDD’14, pages 831–840. ACM, 2014.

[15] Y. Gao, J. Tang, R. Hong, Q. Dai, T. Chua, and R. Jain, ―W2go: A travel guidance system by automatic landmark ranking,‖ in Proc. Int.Conf. Multimedia, 2010, pp. 123–128.

[16] T. Kurashima, T. Tezuka, and K. Tanaka, ―Mining and visualizing local experiences from blog entries,‖ in Database and Expert System Applications. New York, NY, USA: Springer, 2006, pp. 213–222.

[17] H. Kori, S. Hattori, T. Tezuka, and K. Tanaka, ―Automatic generation of multimedia tour guide from local blogs,‖ Adv. Multimedia Model., vol. 4351, no. 1, pp. 690–699, 2006.

[18] Puthankurissi S Raju. Optimum stimulation level: its relationship to personality, demographics, and exploratory behavior. Journal of Consumer Research, pages 272–282, 1980.

[19] Lakshmish Ramaswamy, P Deepak, Ramana Polavarapu, Kutila Gunasekera, Dinesh Garg, Karthik Visweswariah, and Shivkumar Kalyanaraman. Caesar: A context-aware, social recommender system for low-end mobile devices. In MDM, pages 338–347. IEEE, 2009.

[20] Rudy Raymond, Takamitsu Sugiura, and Kota Tsubouchi. Location recommendation based on location history and spatio-temporal correlations for an on-demand bus system. In ACM SIGSPATIAL. ACM, 2011.

[21] L. Song, D. Kotz, R. Jain, and X. He. Evaluating location predictors with extensive wi-fi mobility data. In Proceedings of INFOCOM’04, volume 2, pages 1414–1424. IEEE, 2004.

View Full Article: