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International Conference on Web-Age Information Management

WAIM 2015: Web-Age Information Management pp 41-52 | Cite as

Personalized Mention Probabilistic Ranking – Recommendation on Mention Behavior of Heterogeneous Social Network

  • Quanle LiEmail author
  • Dandan Song
  • Lejian Liao
  • Li Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9391)

Abstract

Selecting a suitable person to mention on the Micro-blogging network, expressed as “@username”, is a new aspect of recommendation system which carries great importance to promote user experience and information propagation. We comprehend information propagation as the reach, vitality, and effectiveness of tweet messages. In this case, we consider this mention recommendation as a probabilistic problem and propose our method named Personalized Mention Probabilistic Ranking to find out who has the maximal capability and possibility to help tweet diffusion by utilizing probabilistic factor graph model in the heterogeneous social network. A wide range of features are extracted and highlighted in our model, such as tag similarity, text similarity, social influence, interaction history and named entities. Experimental results show that our approach outperforms the state-of-art algorithms.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Beijing Institute of TechnologyBeijingChina

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