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 Xu, Lei Zhao, Victor S. Sheng

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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.

Original languageEnglish
Pages (from-to)727-738
Number of pages12
JournalJournal of Computer Science and Technology
Issue number4
StatePublished - Jul 1 2018


  • group recommendation
  • social network
  • topic model

Fingerprint Dive into the research topics of 'A Generative Model Approach for Geo-Social Group Recommendation'. Together they form a unique fingerprint.

Cite this