Personalized Travel Package With Multi-Point-of-Interest Recommendation Based on Crowdsourced User Footprints

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 prefere...

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Vydáno v:IEEE transactions on human-machine systems Ročník 46; číslo 1; s. 151 - 158
Hlavní autoři: Yu, Zhiwen, Xu, Huang, Yang, Zhe, Guo, Bin
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.02.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Entertainment industry
Filtering
Footprints
Location-based social networks (LBSNs)
Man machine systems
Packages
Personalized
Planning
point-of-interest (POI) detection
Poles and towers
Recommendations
Recommender systems
Route planning
Trajectory
travel package recommendation
travel route planning (TRP)
Urban areas
point-of-interest (POI) detection
Location-based social networks (LBSNs)
travel package recommendation
travel route planning (TRP)
ISSN:2168-2291, 2168-2305
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Popis
Shrnutí: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.
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ISSN:2168-2291
2168-2305
DOI:10.1109/THMS.2015.2446953