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

Zhiwen Yu, Huang Xu, Zhe Yang, Bin Guo

Research output: Contribution to journalArticlepeer-review

219 Scopus citations

Abstract

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.

Original languageEnglish
Article number7145457
Pages (from-to)151-158
Number of pages8
JournalIEEE Transactions on Human-Machine Systems
Volume46
Issue number1
DOIs
StatePublished - Feb 2016

Keywords

  • Location-based social networks (LBSNs)
  • point-of-interest (POI) detection
  • travel package recommendation
  • travel route planning (TRP)

Fingerprint

Dive into the research topics of 'Personalized Travel Package with Multi-Point-of-Interest Recommendation Based on Crowdsourced User Footprints'. Together they form a unique fingerprint.

Cite this