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Regular Paper

A Generative Model Approach for Geo-Social Group Recommendation

School of Computer Science and Technology, Soochow University, Suzhou 215006, China
Department of Management Science and Information Systems, Rutgers University, Piscataway, NJ 08854, U.S.A.
Department of Computer Science, University of Central Arkansas, Conway 72035, U.S.A.
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Abstract

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.

Keywords

group recommendationtopic modelsocial network

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Journal of Computer Science and Technology
Volume 33 Issue 4,
July 2018
Pages 727-738
DOI: 10.1007/s11390-018-1852-1
Cite this article:
Zhao P-P, Zhu H-F, Liu Y, et al. A Generative Model Approach for Geo-Social Group Recommendation. Journal of Computer Science and Technology, 2018, 33(4): 727-738. https://doi.org/10.1007/s11390-018-1852-1

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Received: 27 December 2017
Revised: 09 May 2018
Published: 13 July 2018
©2018 LLC & Science Press, China
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