? A Generative Model Approach for Geo-Social Group Recommendation
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Journal of Computer Science and Technology 2018, Vol. 33 Issue (4) :727-738    DOI: 10.1007/s11390-018-1852-1
Special Issue on Software Engineering for High-Confidence Systems Current Issue | Archive | Adv Search << Previous Articles | Next Articles >>
A Generative Model Approach for Geo-Social Group Recommendation
Peng-Peng Zhao1, Member, CCF, ACM, IEEE, Hai-Feng Zhu1, Member, CCF, Yanchi Liu2, Zi-Ting Zhou1 Zhi-Xu Li1, Member, CCF, ACM, IEEE, Jia-Jie Xu1,*, Member, CCF, Lei Zhao1, Member, CCF, Victor S. Sheng3
1 School of Computer Science and Technology, Soochow University, Suzhou 215006, China;
2 Department of Management Science and Information Systems, Rutgers University, Piscataway, NJ 08854, U.S.A.;
3 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.
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Keywordsgroup recommendation   topic model   social network     
Received 2017-12-27;

This research was partially supported by the National Natural Science Foundation of China under Grant No. 61572335 and the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20151223.

Corresponding Authors: Jia-Jie Xu,E-mail:xujj@suda.edu.cn     Email: xujj@suda.edu.cn
About author: Peng-Peng Zhao is an associate professor in the School of Computer Science and Technology at Soochow University, Suzhou. He received his Ph.D. degree in computer science from Soochow University, Suzhou, in 2008. His main research interests are in the study of data integration, spatial data processing, data mining, machine learning and crowdsourcing.
Cite this article:   
Peng-Peng Zhao, Hai-Feng Zhu, Yanchi Liu, Zi-Ting Zhou, Zhi-Xu Li, Jia-Jie Xu, Lei Zhao, Victor S. Sheng.A Generative Model Approach for Geo-Social Group Recommendation[J]  Journal of Computer Science and Technology, 2018,V33(4): 727-738
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