Dual implicit mining-based latent friend recommendation

Lin Cui, Jia Wu, Dechang Pi, Peng Zhang, Paul Kennedy

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The latent friend recommendation in online social media is interesting, yet challenging, because the user-item ratings and the user-user relationships are both sparse. In this paper, we propose a new dual implicit mining-based latent friend recommendation model that simultaneously considers the implicit interest topics of users and the implicit link relationships between the users in the local topic cliques. Specifically, we first propose an algorithm called all reviews from a user and all tags from their corresponding items to learn the implicit interest topics of the users and their corresponding topic weights, then compute the user interest topic similarity using a symmetric Jensen-Shannon divergence. After that, we adopt the proposed weighted local random walk with restart algorithm to analyze the implicit link relationships between the users in the local topic cliques and calculate the weighted link relationship similarity between the users. Combining the user interest topic similarity with the weighted link relationship similarity in a unified way, we get the final latent friend recommendation list. The experiments on real-world datasets demonstrate that the proposed method outperforms the state-of-the-art latent friend recommendation methods under four different types of evaluation metrics.

Original languageEnglish
Pages (from-to)1663-1678
Number of pages16
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume50
Issue number5
DOIs
Publication statusPublished - May 2020

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Keywords

  • Dual implicit mining
  • latent friend recommendation
  • random walk with restart
  • user interest topic

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