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      Prediction of retweet behavior based on multiple trust relationships

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          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.

          Abstract

          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%。

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          Author and article information

          Journal
          J Tsinghua Univ (Sci & Technol)
          Journal of Tsinghua University (Science and Technology)
          Tsinghua University Press
          1000-0054
          15 April 2019
          09 April 2019
          : 59
          : 4
          : 270-275
          Affiliations
          1School of Computer Science and Technology, Shandong University of Technology, Zibo 255091, China
          2Key Laboratory of Data Engineering and Knowledge Engineering, MOE, Information School, Renmin University of China, Beijing 100872, China
          3CAS Key Laboratory of Network Data Science & Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
          Author notes
          *Corresponding author: LI Cuiping, E-mail: licuiping@ 123456ruc.edu.cn
          Article
          j.cnki.qhdxxb.2018.25.060
          10.16511/j.cnki.qhdxxb.2018.25.060
          Copyright © Journal of Tsinghua University

          This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 Unported License (CC BY-NC 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See https://creativecommons.org/licenses/by-nc/4.0/.

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