Item diversified recommendation based on influence diffusion

Huimin Huang, Hong Shen, Zaiqiao Meng

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


Recently, the high popularity of social networks accelerates the development of item recommendation. Integrating the influence diffusion of social networks in recommendation systems is a challenging task since topic distribution over users and items is latent and user topic interest may change over time. In this paper, we propose a dynamic generative model for item recommendation which captures the potential influence logs based on the community-level topic influence diffusion to infer the latent topic distribution over users and items. Our model enables tracking the time-varying distributions of topic interest and topic popularity over communities in social networks. A collapsed Gibbs sampling algorithm is proposed to train the model, and an improved diversification algorithm is proposed to obtain item diversified recommendation list. Extensive experiments are conducted to evaluate the effectiveness and efficiency of our method. The results validate our approach and show the superiority of our method compared with state-of-the-art diversified recommendation methods.

Original languageEnglish
Pages (from-to)939-954
Number of pages16
JournalInformation Processing and Management
Issue number3
Publication statusPublished - May 2019
Externally publishedYes


  • Influence diffusion
  • Item recommendation
  • Social networks


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