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Evolutionary Poisson Factorization Based Multiple Trust Relationships

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Abstract

The task of predicting retweet behavior is the key step for modeling the information diffusion in social networks and also is applied for a variety of fields such as personalized recommendation and topic detection. While much work has been conducted for mining textual content that users generate, existing studies on predictions of retweet behavior have not taken full advantage of the effects of multiple social relationships between users based dynamic Poisson factorization. To achieve this goal, we develop a multiple trust relationship evolutionary Poisson factorization, a new Bayesian factorization model that incorporates multiple trust relationships between users into an evolutionary Poisson factorization model to predict retweeting behavior. This model can obtain the impacts of a variety of social influences for user’s latent preferences as well as models the time evolving latent user and item factors. We studied our models with several large real-world data sets, and the experiments show the superior performance of our proposed method compared with the state-of-the-art approaches.

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Funding

This work is supported by the National Natural Science Foundation of China no. 61672272; Key scientific research platform of Guangdong Provincial University no. 2020ZDZX3033; Scientific and Technological Project of Zhanjiang nos. 2020B01272, 2020B01252; Lingnan Normal University Scientific and Technological Project of YB2105; the project of human social science of Guangdong Provincial no. GD20XXW05.

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Authors and Affiliations

  1. Business School, Lingnan Normal University, 524048, Xhanjiang, China

    Chunyan Yin

  2. School of Foreign Studies, Lingnan Normal University, 524048, Zhanjiang, China

    Rui Zhang

  3. School of Information Engineering, Lingnan Normal University, 524048, Zhanjiang, China

    Yongheng Chen

Authors
  1. Chunyan Yin
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  2. Rui Zhang
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  3. Yongheng Chen
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Corresponding author

Correspondence to Rui Zhang.

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COMPLIANCE WITH ETHICAL STANDARDS

This article is a completely original work of its authors; it has not been published before and will not be sent to other publications until the PRIA Editorial Board decides not to accept it for publication.

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The authors declare that they have no conflicts of interest.

Additional information

Rui Zhang. She received the Doctor’s degree at the Department of Human Sciences, majoring Japanese Language Education, Tokyo Metropolitan University in Japan in 2018. She is an university lecturer at School of Foreign Studies, Lingnan Normal University. She is working in Lingnan Normal University. Her main research area covers information integration, machine translation.

Yong-Heng Chen. He received the PhD degree at the Department of Computer Science and Technology, Jilin University in 2012. Since August 2018, he is a professor at School of Information Engineering, Lingnan Normal University. His current main research interests include data mining, web intelligence, and ontology engineering and information integration. He is a Member of System Software Committee of China’s Computer Federation. More than 20 papers of him were published in journals or international conferences.

Chun-yan Yin. She obtained the BS degree from Harbin Normal University. She is an university lecturer at Business School, Lingnan Normal University. Main research area covers database theory, machine learning, data mining, and granular computing.

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Chunyan Yin, Zhang, R. & Chen, Y. Evolutionary Poisson Factorization Based Multiple Trust Relationships. Pattern Recognit. Image Anal. 32, 218–227 (2022). https://doi.org/10.1134/S1054661822010126

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