? 一种地理社交组推荐的生成模型方法
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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 << Previous Articles | Next Articles >>
一种地理社交组推荐的生成模型方法
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.
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.

摘要
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摘要 随着在线社交网络的发展与普及,人们乐意与朋友或熟人参加、分享组活动,这已经成为一种潮流。这激发了旨在满足一组用户需求而非仅仅是个人用户的组推荐的研究。然而,如何融合组成员的不同偏好仍然是具有挑战性的问题:1)组成员的偏好选择受多种因素的影响,即个人偏好,组的主题和社交关系;2)用户在不同组中影响力不同。本文我们提出一个生成地理社交组推荐模型(GSGR)来为组推荐兴趣点(POI)。具体而言,GSGR很好地模拟建模了受地理信息影响的个人偏好,组主题和社交因素。此外,在推荐部分,GSGR融合了具有不同权重的组成员的偏好来估计组对POI的偏好得分。最后,两个数据集上的实验结果显示,GSGR在组推荐中是有效的,并且胜过了目前最先进的方法。
关键词组推荐   主题模型   社交网络     
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.
Keywordsgroup recommendation   topic model   social network     
Received 2017-12-27;
本文基金:

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.

通讯作者: 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.
引用本文:   
Peng-Peng Zhao, Hai-Feng Zhu, Yanchi Liu, Zi-Ting Zhou, Zhi-Xu Li, Jia-Jie Xu.一种地理社交组推荐的生成模型方法[J]  Journal of Computer Science and Technology , 2018,V33(4): 727-738
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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http://jcst.ict.ac.cn:8080/jcst/CN/10.1007/s11390-018-1852-1
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