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Mention Recommendation with Context-Aware Probabilistic Matrix Factorization

  • Bo Jiang
  • Zhigang LuEmail author
  • Ning Li
  • Zelin Cui
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11537)

Abstract

Mention as a key feature on social networks can break through the effect of structural trapping and expand the visibility of a message. Although existing works usually use rank learning as implementation strategy before performing mention recommendation, these approaches may interfere with the influening factor exploration and cause some biases. In this paper, we propose a novel Context-aware Mention recommendation model based on Probabilistic Matrix Factorization (CMPMF). This model considers four important mention contextual factors including topic relevance, mention affinity, user profile similarity and message semantic similarity to measure the relevance score from users and messages dimensions. We fuse these mention contextual factors in latent spaces into the framework of probabilistic matrix factorization to improve the performance of mention recommendation. Through evaluation on a real-world dataset from Weibo, the empirically study demonstrates the effectiveness of discovered mention contextual factors. We also observe that topic relevance and mention affinity play a much significant role in the mention recommendation task. The results demonstrate our proposed method outperforms the state-of-the-art algorithms.

Keywords

Mention recommendation Social network Probabilistic Matrix Factorization Contextual information 

Notes

Acknowledgments

This work is supported by Natural Science Foundation of China (No. 61702508, No. 61802404), and Strategic Priority Research Program of CAS (XDC02040100, XDC02030200). This work is also partially supported by Key Laboratory of Network Assessment Technology, Chinese Academy of Sciences and Beijing Key Laboratory of Network security and Protection Technology.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Information EngineeringChinese Academy of SciencesBeijingChina
  2. 2.School of Cyber SecurityUniversity of Chinese Academy of SciencesBeijingChina

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