Journal of Computer Science and Technology

, Volume 33, Issue 4, pp 727–738 | Cite as

A Generative Model Approach for Geo-Social Group Recommendation

  • Peng-Peng Zhao
  • Hai-Feng Zhu
  • Yanchi Liu
  • Zi-Ting Zhou
  • Zhi-Xu Li
  • Jia-Jie XuEmail author
  • Lei Zhao
  • Victor S. Sheng
Regular Paper


With the development and prevalence of online social networks, there is an obvious tendency that people are willing to attend and share group activities with friends or acquaintances. This motivates the study on group recommendation, which aims to meet the needs of a group of users, instead of only individual users. However, how to aggregate different preferences of different group members is still a challenging problem: 1) the choice of a member in a group is influenced by various factors, e.g., personal preference, group topic, and social relationship; 2) users have different influences when in different groups. In this paper, we propose a generative geo-social group recommendation model (GSGR) to recommend points of interest (POIs) for groups. Specifically, GSGR well models the personal preference impacted by geographical information, group topics, and social influence for recommendation. Moreover, when making recommendations, GSGR aggregates the preferences of group members with different weights to estimate the preference score of a group to a POI. Experimental results on two datasets show that GSGR is effective in group recommendation and outperforms the state-of-the-art methods.


group recommendation topic model social network 


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Peng-Peng Zhao
    • 1
  • Hai-Feng Zhu
    • 1
  • Yanchi Liu
    • 2
  • Zi-Ting Zhou
    • 1
  • Zhi-Xu Li
    • 1
  • Jia-Jie Xu
    • 1
    Email author
  • Lei Zhao
    • 1
  • Victor S. Sheng
    • 3
  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.Department of Management Science and Information SystemsRutgers UniversityPiscatawayU.S.A.
  3. 3.Department of Computer ScienceUniversity of Central ArkansasConwayU.S.A.

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