Search result diversification on attributed networks via nonnegative matrix factorization

Zaiqiao Meng, Hong Shen, Huimin Huang, Wei Liu, Jing Wang, Arun Kumar Sangaiah

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


Search result diversification is an effective way to tackle query ambiguity and enhance result novelty. In the context of large information networks, diversifying search result is also critical for further design of applications such as link prediction and citation recommendation. In previous work, this problem has mainly been tackled in a way of implicit query intent. To further enhance the performance on attributed networks, we propose a novel search result diversification approach via nonnegative matrix factorization. Our approach encodes latent query intents as well as nodes as representation vectors by a novel nonnegative matrix factorization model, and the diversity of the results accounts for the query relevance and the novelty w.r.t. these vectors. To learn the representation vectors of nodes, we derive the multiplicative updating rules to train the nonnegative matrix factorization model. We perform a comprehensive evaluation on our approach with various baselines. The results show the effectiveness of our proposed solution, and verify that attributes do help improve diversification performance.

Original languageEnglish
Pages (from-to)1277-1291
Number of pages15
JournalInformation Processing and Management
Issue number6
Publication statusPublished - Nov 2018
Externally publishedYes


  • Attributed network
  • Diversification
  • Graph search
  • Nonnegative matrix factorization


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