TY - GEN
T1 - A framework for cloud-based POI search and trip planning systems
AU - Ying, Jia Ching
AU - Lu, Eric Hsueh Chan
AU - Huang, Chi Min
AU - Kuo, Kuan Cheng
AU - Hsiao, Yu Hsien
AU - Tseng, S.
PY - 2013/7/12
Y1 - 2013/7/12
N2 - In recent years, researches on Location-Based service have attracted extensive attentions due to the wide applications. Among them, one of the active topics is Cloud-based Trip Planning for meeting user's personal requirements. Although a number of studies on this topic have been proposed in literatures, most of them only regard the user-specific constraints as some filtering conditions for planning the trip. In fact, immersing the constraints into travel recommendation systems to provide a personalized trip is desired for users. Furthermore, time complexity of trip planning from a set of attractions is sensitive to the scalability of travel regions. Hence, how to reduce the computational cost by parallel cloud computing techniques is also a critical issue. In this paper, we propose a novel system named Touch Map to efficiently recommend the personalized trips meeting multiple constraints of users by mining user's check-in behaviors. In Touch Map, a POI search module is first proposed to select POIs which are desired for user-specific constraints. Then, we adopt our previous work, Trip-Mine, to efficiently plan the trip that satisfies multiple user-specific constraints. As a whole, we propose a novel framework for cloud-based travel recommendation that considers the issues of multiple constraints. Through comprehensive experimental evaluations, PTR is shown to deliver excellent performance.
AB - In recent years, researches on Location-Based service have attracted extensive attentions due to the wide applications. Among them, one of the active topics is Cloud-based Trip Planning for meeting user's personal requirements. Although a number of studies on this topic have been proposed in literatures, most of them only regard the user-specific constraints as some filtering conditions for planning the trip. In fact, immersing the constraints into travel recommendation systems to provide a personalized trip is desired for users. Furthermore, time complexity of trip planning from a set of attractions is sensitive to the scalability of travel regions. Hence, how to reduce the computational cost by parallel cloud computing techniques is also a critical issue. In this paper, we propose a novel system named Touch Map to efficiently recommend the personalized trips meeting multiple constraints of users by mining user's check-in behaviors. In Touch Map, a POI search module is first proposed to select POIs which are desired for user-specific constraints. Then, we adopt our previous work, Trip-Mine, to efficiently plan the trip that satisfies multiple user-specific constraints. As a whole, we propose a novel framework for cloud-based travel recommendation that considers the issues of multiple constraints. Through comprehensive experimental evaluations, PTR is shown to deliver excellent performance.
KW - Cloud-based Recommendation
KW - Data Mining
KW - Location-Based Social Network
KW - Trip Planning
UR - http://www.scopus.com/inward/record.url?scp=84879871981&partnerID=8YFLogxK
U2 - 10.1109/ICOT.2013.6521211
DO - 10.1109/ICOT.2013.6521211
M3 - Conference contribution
AN - SCOPUS:84879871981
SN - 9781467359368
T3 - ICOT 2013 - 1st International Conference on Orange Technologies
SP - 274
EP - 277
BT - ICOT 2013 - 1st International Conference on Orange Technologies
Y2 - 12 March 2013 through 16 March 2013
ER -