Travel route planning is one of the most important steps for a tourist to prepare his/her trip. Before traveling to an unknown location, most people have questions about how to plan their trips. Although users can take help of travel guide or ask questions to web based communities, the process is generally not efficient and the results may not be customized. An automatic and interactive travel route planning service is highly desired to plan a customized trip according to users’ preferences. This paper provides personalized travel sequence recommendation with the help of both travelogue and community contributed photos. Travelogue websites offers rich descriptions about landmarks and traveling experience written by users. Furthermore, community-contributed photos with metadata (e.g., tags, date taken, latitude etc.) on social media record users’ daily life and travel experience. These data are not only useful for reliable POIs (points of interest) mining, travel routes mining, but give an opportunity to recommend personalized travel POIs and routes based on user’s interest. Compared with general routes recommendation, our recommended personalized travel sequential POIs are more relevant to user’s interest and more convenient for travel planning. We propose Topical Package Model (TPM) method to learn users and route’s travel attributes. It bridges the gap of user interest and routes attributes. We map both user’s and routes’ textual descriptions to the topical package space to get user topical package model and route topical package model (i.e., topical interest, cost, time and season).[9]To recommend personalized POI sequence, first, famous 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.
Shuhui Jiang, Xueming Qian “Pesonalized travel sequence recommendation on multisource big social media”, IEEE transaction on big data, vol.x.x, 2016
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
Y. Shi, P. Serdyukov, A. Hanjalic, and M. Larson, “Personalized landmark recommendation based on geo-tags from photo sharing sites,” ICWSM, vol. 11, pp. 622–625, 2011.
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.
Y. Zheng, L. Zhang, X. Xie, and W. Ma, “Mining interesting locations and travel sequences from gps trajectories,” in Proceedings of the 18th international conference on World wide web. ACM, 2009, pp. 791–800.
H. Huang and G. Gartner, “Using trajectories for collaborativefiltering based poirecommendation,” International Journal of Data Mining, Modelling and Management, vol. 6, no. 4, pp. 333–346, 2014.
X. Lu, C. Wang, J. Yang, Y. Pang, and L. Zhang, “Photo2trip: generating travel routes from geo-tagged photos for trip planning,”in Proceedings of the international conference on Multimedia. ACM, 2010, pp. 143–152.
A. Cheng, Y. Chen, Y. Huang, W. Hsu, and H. Liao, “Personalized travel recommendation by mining people attributes from community-contributed photos,” in Proceedings of the 19th ACM international conference on Multimedia. ACM, 2011, pp. 83-92.
Y. Ge, Q. Liu, H. Xiong, A. Tuzhilin, and J. Chen,“Cost-aware travel tour recommendation,” in Proceedings of the 17th ACMinter- national conference on Knowledge discovery and data mining. ACM, 2011, pp. 983–991.
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, 2015.
V. W. Zheng, Y. Zheng, X. Xie, and Q. Yang, “Collaborative location and activity recommendations with gps history data,” in Proceedings of the 19th international conference on World Wide Web. ACM, 2010, pp. 1029–1038.
Q. Yuan, G. Cong, and A. Sun, “Graph-based point-of-interest recommendation with geographical and temporal influences,” in Proceedings of the 23rd ACM International Conference on Information and Knowledge Management. ACM, 2014, pp. 659–668.
travel recommendation, topical package model, photo collection and information retrieval.
