A vertex similarity index for better personalized recommendation

Ling Jiao Chen, Zi Ke Zhang, Jin Hu Liu, Jian Gao*, Tao Zhou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


Recommender systems benefit us in tackling the problem of information overload by predicting our potential choices among diverse niche objects. So far, a variety of personalized recommendation algorithms have been proposed and most of them are based on similarities, such as collaborative filtering and mass diffusion. Here, we propose a novel vertex similarity index named CosRA, which combines advantages of both the cosine index and the resource-allocation (RA) index. By applying the CosRA index to real recommender systems including MovieLens, Netflix and RYM, we show that the CosRA-based method has better performance in accuracy, diversity and novelty than some benchmark methods. Moreover, the CosRA index is free of parameters, which is a significant advantage in real applications. Further experiments show that the introduction of two turnable parameters cannot remarkably improve the overall performance of the CosRA index.

Original languageEnglish (US)
Pages (from-to)607-615
Number of pages9
JournalPhysica A: Statistical Mechanics and its Applications
StatePublished - Jan 15 2017


  • Information filtering
  • Personalized recommendations
  • Recommender systems
  • Vertex similarity

ASJC Scopus subject areas

  • Statistics and Probability
  • Condensed Matter Physics


Dive into the research topics of 'A vertex similarity index for better personalized recommendation'. Together they form a unique fingerprint.

Cite this