Definition
Generally speaking, the “mobility” in users’ (i.e., tourists’) travel tour comes from both the users (i.e., they intend/plan to move from place to place) and their devices (i.e., some of the contexts of the user mobility may be recorded by their mobile devices). By mining these mobilities, either explicitly (interacting with the users) or implicitly (learning from user profiles and historical records), mobile travel tour recommendation aims to precisely and efficiently recommend users the destinations (e.g., Place of Interests (POI)) to visit, the routes to take, the attractive packages to choose, some other context-aware information (e.g., the events/activities nearby), etc. As a privileged type of recommender system, mobile travel tour recommendation has become a valuable tool to deal with the information overload problem in tourism.
Historical Background
Mobile travel tour recommendation...
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Agrawal R, Imieliński T, Swami A (1993) Mining association rules between sets of items in large databases. In: ACM SIGMOD record, Washington, DC, vol 22. ACM, pp 207–216
Baltrunas L, Ludwig B, Ricci F (2011) Matrix factorization techniques for context aware recommendation. In: Proceedings of the fifth ACM conference on recommender systems. ACM, New York, pp 301–304
Bao T, Cao H, Chen E, Tian J, Xiong H (2012) An unsupervised approach to modeling personalized contexts of mobile users. Knowl Inf Syst 31(2):345–370
Burdick D, Calimlim M, Gehrke J (2001) Mafia: a maximal frequent itemset algorithm for transactional databases. In: Proceedings of 17th international conference on data engineering, Heidelberg. IEEE, Los Alamitos, pp 443–452
Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G (2014) Mobile recommender systems in tourism. J Netw Comput Appl 39:319–333
Ge Y, Xiong H, Tuzhilin A, Xiao K, Gruteser M, Pazzani M (2010) An energy-efficient mobile recommender system. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, San Jose. ACM, New York, pp 899–908
Giannotti F, Nanni M, Pinelli F, Pedreschi D (2007) Trajectory pattern mining. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 330–339
Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. In: ACM SIGMOD record, Dallas, vol 29. ACM, New York, pp 1–12
Kenteris M, Gavalas D, Economou D (2011) Electronic mobile guides: a survey. Pers Ubiquitous Comput 15(1):97–111
Lee HJ, Park SJ (2007) Moners: a news recommender for the mobile web. Expert Syst Appl 32(1):143–150
Lian D, Zhao C, Xie X, Sun G, Chen E, Rui Y (2014) Geomf: joint geographical modeling and matrix factorization for point-of-interest recommendation. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 831–840
Liu B, Xiong H (2013) Point-of-interest recommendation in location based social networks with topic and location awareness. In: SDM, Austin. SIAM, pp 396–404
Liu Q, Ge Y, Li Z, Chen E, Xiong H (2011) Personalized travel package recommendation. In: 2011 IEEE 11th international conference on data mining (ICDM), Vancouver. IEEE, Los Alamitos, pp 407–416
Liu B, Fu Y, Yao Z, Xiong H (2013a) Learning geographical preferences for point-of-interest recommendation. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, Chicago. ACM, New York, pp 1043–1051
Liu Q, Ma H, Chen E, Xiong H (2013b) A survey of context-aware mobile recommendations. Int J Inf Technol Decis Mak 12(01):139–172
Liu Q, Chen E, Xiong H, Ge Y, Li Z, Wu X (2014) A cocktail approach for travel package recommendation. IEEE Trans Knowl Data Eng 26(2):278–293
Pang-Ning T, Steinbach M, Kumar V et al (2006) Introduction to data mining. In: Library of congress. Addison Wesley
Pasquier N, Bastide Y, Taouil R, Lakhal L (1999) Discovering frequent closed itemsets for association rules. In: Database Theory ICDT 99, Jerusalem. Springer, Berlin/New York, pp 398–416
Quercia D, Lathia N, Calabrese F, Di Lorenzo G, Crowcroft J (2010) Recommending social events from mobile phone location data. In: 2010 IEEE 10th international conference on data mining (ICDM), Sydney. IEEE, Los Alamitos, pp 971–976
Ricci F (2010) Mobile recommender systems. Inf Technol Tour 12(3):205–231
Ricci F, Rokach L, Shapira B (2011) Introduction to recommender systems handbook. Springer, New York
Srivastava J, Cooley R, Deshpande M, Tan P-N (2000) Web usage mining: discovery and applications of usage patterns from web data. ACM SIGKDD Explor Newsl 1(2):12–23
Ye M, Shou D, Lee W-C, Yin P, Janowicz K (2011) On the semantic annotation of places in location-based social networks. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 520–528
Yuan J, Zheng Y, Zhang C, Xie W, Xie X, Sun G, Huang Y (2010) T-drive: driving directions based on taxi trajectories. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems, San Jose. ACM, New York, pp 99–108
Yuan J, Zheng Y, Xie X (2012) Discovering regions of different functions in a city using human mobility and pois. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, Beijing. ACM, New York, pp 186–194
Zheng Y, Xie X (2011) Learning travel recommendations from user-generated GPS traces. ACM Trans Intell Syst Technol 2(1):2
Zheng Y, Liu L, Wang L, Xie X (2008) Learning transportation mode from raw gps data for geographic applications on the web. In: Proceedings of the 17th international conference on World Wide Web, Beijing. ACM, New York, pp 247–256
Zheng VW, Zheng Y, Xie X, Yang Q (2012) Towards mobile intelligence: learning from GPS history data for collaborative recommendation. Artif Intell 184:17–37
Zhu H, Chen E, Xiong H, Yu K, Cao H, Tian J (2013, to appear) Mining mobile user preferences for personalized context-aware recommendation. ACM Trans Intell Syst Technol 5(4):1–27
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Liu, Q. (2017). Mobile Travel Tour Recommendation. In: Shekhar, S., Xiong, H., Zhou, X. (eds) Encyclopedia of GIS. Springer, Cham. https://doi.org/10.1007/978-3-319-17885-1_1519
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