TY - GEN
T1 - TripCloud
T2 - 13th International Symposium on Spatial and Temporal Databases, SSTD 2013
AU - Ying, Josh Jia Ching
AU - Lu, Eric Hsueh Chan
AU - Shi, Bo Nian
AU - Tseng, Vincent S.
PY - 2013
Y1 - 2013
N2 - With the advance of Location-Based Services (LBS), researches on trip recommendation have attracted extensive attentions. Among them, one active topic is trip planning. In the previous studies on trip planning, various user constraints such as travel time, travel budget, attraction categories, etc., have been considered and users' past travel logs were analyzed for travel recommendation. However, such kind of trip planning approaches cause the computational complexity to increase significantly. Hence, in this paper, we demonstrate a cloud-based travel recommendation system named TripCloud, which is built by extending our previous work, Personalized Trip Recommendation (PTR), for meeting user's multiple constraints with efficient trip planning. TripCloud encapsulates several data mining techniques and a cloud-based trip planning model to rate the interestingness of each attraction and plan an interesting trip, respectively. Visualization interface is also provided to exhibit the recommended trips based on the characteristics of user constraints.
AB - With the advance of Location-Based Services (LBS), researches on trip recommendation have attracted extensive attentions. Among them, one active topic is trip planning. In the previous studies on trip planning, various user constraints such as travel time, travel budget, attraction categories, etc., have been considered and users' past travel logs were analyzed for travel recommendation. However, such kind of trip planning approaches cause the computational complexity to increase significantly. Hence, in this paper, we demonstrate a cloud-based travel recommendation system named TripCloud, which is built by extending our previous work, Personalized Trip Recommendation (PTR), for meeting user's multiple constraints with efficient trip planning. TripCloud encapsulates several data mining techniques and a cloud-based trip planning model to rate the interestingness of each attraction and plan an interesting trip, respectively. Visualization interface is also provided to exhibit the recommended trips based on the characteristics of user constraints.
KW - Cloud Computing
KW - Data Mining
KW - Location-Based Social Network
KW - Recommendation Techniques
KW - Trip Planning
UR - http://www.scopus.com/inward/record.url?scp=84881237560&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-40235-7_31
DO - 10.1007/978-3-642-40235-7_31
M3 - Conference contribution
AN - SCOPUS:84881237560
SN - 9783642402340
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 472
EP - 477
BT - Advances in Spatial and Temporal Databases - 13th International Symposium, SSTD 2013, Proceedings
Y2 - 21 August 2013 through 23 August 2013
ER -