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Group Trip Recommendation Systems

  • Hua-Hong Huang
  • Sheng-Min Chiu
  • Yi-Chung Chen
  • Chiang Lee
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 886)

Abstract

Many of the most popular tourist attraction recommendation systems use the personal profiles of users from social networks. However, most of these services focus on individuals, rather than group activities with friends and family, despite the fact that check-in data includes accompanying members. In this study, we developed a recommendation system specifically for groups of users. Experiments demonstrate the efficacy of the proposed algorithm in making group recommendations based on the Foursquare dataset, with performance exceeding that of baseline methods.

Keywords

Group recommendation Recommendation system Foursquare 

Notes

Acknowledgments

This work was supported in part by the Ministry of Science and Technology of Taiwan, R.O.C., under Contracts MOST 106-2119-M-224-003 and MOST 106-2221-E-006-247.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hua-Hong Huang
    • 1
  • Sheng-Min Chiu
    • 1
  • Yi-Chung Chen
    • 2
  • Chiang Lee
    • 1
  1. 1.Department of Computer, Science and Information EngineeringNational Cheng Kung UniversityTainanTaiwan
  2. 2.Department of Industry Engineering and ManagementNational Yunlin University of Science and TechnologyYunlinTaiwan

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