Please wait a minute...
 首页  期刊介绍 期刊订阅 联系我们 横山亮次奖 百年刊庆
 
最新录用  |  预出版  |  当期目录  |  过刊浏览  |  阅读排行  |  下载排行  |  引用排行  |  横山亮次奖  |  百年刊庆
清华大学学报(自然科学版)  2019, Vol. 59 Issue (4): 270-275    DOI: 10.16511/j.cnki.qhdxxb.2018.25.060
  计算机科学与技术 本期目录 | 过刊浏览 | 高级检索 |
基于多重信任关系的微博转发行为预测
王绍卿1,2, 李翠平2, 王征3, 陈红2
1. 山东理工大学 计算机科学与技术学院, 淄博 255091;
2. 中国人民大学 信息学院, 数据工程与知识工程教育部重点实验室, 北京 100872;
3. 中国科学院计算技术研究所, 中国科学院网络数据科学与技术重点实验室, 北京 100190
Prediction of retweet behavior based on multiple trust relationships
WANG Shaoqing1,2, LI Cuiping2, WANG Zheng3, CHEN Hong2
1. School of Computer Science and Technology, Shandong University of Technology, Zibo 255091, China;
2. Key Laboratory of Data Engineering and Knowledge Engineering, MOE, Information School, Renmin University of China, Beijing 100872, China;
3. CAS Key Laboratory of Network Data Science & Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
全文: PDF(1422 KB)  
输出: BibTeX | EndNote (RIS)      
摘要 微博转发预测有助于热点话题检测、个性化微博推荐等,近些年引起了学术界和工业界的广泛关注。然而,现有的关于微博转发预测的研究工作没有充分利用用户之间的多重信任关系的影响。该文提出联合概率模型,把用户之间的多重信任关系融入传统的Bayesian Poisson因子分解(Bayesian Poisson factorization,BPF)模型,从而预测转发行为。该模型命名为TrustBPF,可以灵活地捕获用户之间的各种社交影响。该文进一步把用户之间的信任强度整合到一个框架中。在新浪微博数据集上验证结果表明:在NDCG@3和Precision@3指标上,TrustBPF模型比原始的BPF模型分别提升了90.91%和88.37%。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
王绍卿
李翠平
王征
陈红
关键词 Poisson因子分解转发预测信任关系社交网络    
Abstract:In recent years, predictions of retweet behavior have attracted growing attention from both academic and industrial fields since the retweets can help with a number of tasks such as hot topic detection and personalized tweet recommendations. However, existing studies on predictions of retweet behavior have not taken full advantage of the effects of multiple social relationships between users. This paper presents a probabilistic model which incorporates multiple trust relationships between users into a traditional Bayesian Poisson factorization (BPF) model to predict retweet behavior. This model can capture the effects of a variety of social influences and integrates user trust strengths into the framework. Tests on the Sina Weibo dataset demonstrate that the model gives 90.91% improvement in the NDCG@3 score and 88.37% improvement in the precision@3 score over a traditional BPF.
Key wordsPoisson factorization    retweet prediction    trust relationships    social network
收稿日期: 2018-07-18      出版日期: 2019-04-09
基金资助:国家重点研发计划资助项目(2016YFB1000702);国家自然科学基金资助项目(61772537,61772536,61702522,61532021)
通讯作者: 李翠平,教授,E-mail:licuiping@ruc.edu.cn     E-mail: licuiping@ruc.edu.cn
引用本文:   
王绍卿, 李翠平, 王征, 陈红. 基于多重信任关系的微博转发行为预测[J]. 清华大学学报(自然科学版), 2019, 59(4): 270-275.
WANG Shaoqing, LI Cuiping, WANG Zheng, CHEN Hong. Prediction of retweet behavior based on multiple trust relationships. Journal of Tsinghua University(Science and Technology), 2019, 59(4): 270-275.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2018.25.060  或          http://jst.tsinghuajournals.com/CN/Y2019/V59/I4/270
  表1 新浪微博数据集的统计数据
  图1 用户之间的多重信任关系
  图2 TrustBPF的图模型表示
  表2 TrustBPF: 潜在变量、 完全条件和变分参数
  图3 坐标上升算法
  表3 不同模型的性能对比
  表4 不同信任关系的影响
[1] SU S, WANG Y K, ZHANG Z B, et al. Identifying and tracking topic-level influencers in the microblog streams[J]. Machine Learning, 2018, 107(3):551-578.
[2] DE MAIO C, FENZA G, GALLO M, et al. Time-aware adaptive tweets ranking through deep learning[J]. Future Generation Computer Systems, 2017, doi:10.1016/j.future.2017.07.039.
[3] 仲兆满, 管燕, 胡云, 等. 基于背景和内容的微博用户兴趣挖掘[J]. 软件学报, 2017, 28(2):278-291. ZHONG Z M, GUAN Y, HU Y, et al. Mining user interests on microblog based on profile and content[J]. Journal of Software, 2017, 28(2):278-291. (in Chinese)
[4] 李洋, 陈毅恒, 刘挺. 微博信息传播预测研究综述[J]. 软件学报, 2016, 27(2):247-263. LI Y, CHEN Y H, LIU T. Survey on predicting information propagation in microblogs[J]. Journal of Software, 2016, 27(2):247-263. (in Chinese)
[5] 刘玮, 贺敏, 王丽宏, 等. 基于用户行为特征的微博转发预测研究[J]. 计算机学报, 2016, 39(10):1992-2006. LIU W, HE M, WANG L H, et al. Research on microblog retweeting prediction based on user behavior features[J]. Chinese Journal of Computers, 2016, 39(10):1992-2006. (in Chinese)
[6] JIANG B, LIANG J G, SHA Y, et al. Message clustering based matrix factorization model for retweeting behavior prediction[C]//Proceedings of the 24th ACM CIKM International on Conference on Information and Knowledge Management. New York:ACM, 2015:1843-1846.
[7] ZHANG Q, GONG Y Y, GUO Y, et al. Retweet behavior prediction using hierarchical dirichlet process[C]//Proceedings of the 29th AAAI Conference on Artificial Intelligence. Austin, Texas:AAAI, 2015:403-409.
[8] ZHANG J, TANG J, ZHONG Y Y, et al. Structinf:Mining structural influence from social streams[C]//Proceedings of the 31st AAAI Conferences on Artificial Intelligence. San Francisco, California, USA:AAAI, 2017:73-80.
[9] GOPALAN P, HOFMAN J M, BLEI D M. Scalable recommendation with hierarchical Poisson factorization[C]//Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence. Arlington, Virginia:AUAI Press, 2015:326-335.
[10] ZHANG J, TANG J, LI J Z, et al. Who influenced you? Predicting retweet via social influence locality[J]. ACM Transactions on Knowledge Discovery from Data (TKDD), 2015, 9(3):25.
[11] JIANG B, LIANG J G, SHA Y, et al. Retweeting behavior prediction based on one-class collaborative filtering in social networks[C]//Proceedings of the 39th SIGIR International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM, 2016:977-980.
[12] CEMGIL A T. Bayesian inference for nonnegative matrix factorisation models[J]. Computational Intelligence and Neuroscience, 2009, 2009:785152.
[13] HOFFMAN M, BLEI D M, WANG C, et al. Stochastic variational inference[J]. Journal of Machine Learning Research, 2013, 14(1):1303-1347.
[14] RENDLE S. Factorization machines[C]//Proceedings of the 10th IEEE International Conference on Data Mining. Sydney, NSW, Australia:IEEE, 2010:995-1000.
[15] CHANEY A J B, BLEI D M, ELIASSI-RAD T. A probabilistic model for using social networks in personalized item recommendation[C]//Proceedings of the 9th ACM Conference on Recommender Systems. New York:ACM, 2015:43-50.
[1] 张雪芹, 刘岗, 王智能, 罗飞, 吴建华. 基于多特征融合和深度学习的微观扩散预测[J]. 清华大学学报(自然科学版), 2024, 64(4): 688-699.
[2] 朱唯一, 张雪芹, 顾春华. 基于EDLATrust算法的社交网络信息泄露节点概率预测[J]. 清华大学学报(自然科学版), 2022, 62(2): 355-366.
[3] 屠守中, 杨婧, 赵林, 朱小燕. 半监督的微博话题噪声过滤方法[J]. 清华大学学报(自然科学版), 2019, 59(3): 178-185.
[4] 严素蓉, 冯小青, 廖一星. 基于矩阵分解的社会化推荐模型[J]. 清华大学学报(自然科学版), 2016, 56(7): 793-800.
[5] 朱涵钰, 吴联仁, 吕廷杰. 社交网络用户隐私量化研究: 建模与实证分析[J]. 清华大学学报(自然科学版), 2014, 54(3): 402-406.
[6] 韩心慧, 肖祥全, 张建宇, 刘丙双, 张缘. 基于社交关系的DHT网络Sybil攻击防御[J]. 清华大学学报(自然科学版), 2014, 54(1): 1-7.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 《清华大学学报(自然科学版)》编辑部
本系统由北京玛格泰克科技发展有限公司设计开发 技术支持:support@magtech.com.cn