TY - JOUR
T1 - Personalized Travel Package with Multi-Point-of-Interest Recommendation Based on Crowdsourced User Footprints
AU - Yu, Zhiwen
AU - Xu, Huang
AU - Yang, Zhe
AU - Guo, Bin
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2016/2
Y1 - 2016/2
N2 - Location-based social networks (LBSNs) provide people with an interface to share their locations and write reviews about interesting places of attraction. The shared locations form the crowdsourced digital footprints, in which each user has many connections to many locations, indicating user preference to locations. In this paper, we propose an approach for personalized travel package recommendation to help users make travel plans. The approach utilizes data collected from LBSNs to model users and locations, and it determines users' preferred destinations using collaborative filtering approaches. Recommendations are generated by jointly considering user preference and spatiotemporal constraints. A heuristic search-based travel route planning algorithm was designed to generate travel packages. We developed a prototype system, which obtains users' travel demands from mobile client and generates travel packages containing multiple points of interest and their visiting sequence. Experimental results suggest that the proposed approach shows promise with respect to improving recommendation accuracy and diversity.
AB - Location-based social networks (LBSNs) provide people with an interface to share their locations and write reviews about interesting places of attraction. The shared locations form the crowdsourced digital footprints, in which each user has many connections to many locations, indicating user preference to locations. In this paper, we propose an approach for personalized travel package recommendation to help users make travel plans. The approach utilizes data collected from LBSNs to model users and locations, and it determines users' preferred destinations using collaborative filtering approaches. Recommendations are generated by jointly considering user preference and spatiotemporal constraints. A heuristic search-based travel route planning algorithm was designed to generate travel packages. We developed a prototype system, which obtains users' travel demands from mobile client and generates travel packages containing multiple points of interest and their visiting sequence. Experimental results suggest that the proposed approach shows promise with respect to improving recommendation accuracy and diversity.
KW - Location-based social networks (LBSNs)
KW - point-of-interest (POI) detection
KW - travel package recommendation
KW - travel route planning (TRP)
UR - http://www.scopus.com/inward/record.url?scp=84936135407&partnerID=8YFLogxK
U2 - 10.1109/THMS.2015.2446953
DO - 10.1109/THMS.2015.2446953
M3 - 文章
AN - SCOPUS:84936135407
SN - 2168-2291
VL - 46
SP - 151
EP - 158
JO - IEEE Transactions on Human-Machine Systems
JF - IEEE Transactions on Human-Machine Systems
IS - 1
M1 - 7145457
ER -