A novel method on information recommendation via hybrid similarity

Qin Zhao, Cheng Wang, Pengwei Wang, Mengchu Zhou, Changjun Jiang

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Link similarity is widely applied in measuring the similarity between such objects as Web pages, scientific papers, and social networks. However, there are some deficiencies in the existing methods to measure it. For example, they cannot handle some semantic-similar contents. Their computation may not lead to accurate results in some cases. This paper presents a novel method to do so. It introduces the semantic similarity to calculate the similarity between two given objects, and overcomes the drawback caused by the fact that the existing methods ignore the semantic information of objects. It also gives a novel computation function to make the computing result of similarity more accurate.

Original languageEnglish (US)
Article number7792740
Pages (from-to)448-459
Number of pages12
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume48
Issue number3
DOIs
StatePublished - Mar 2018

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • Data mining
  • Information recommendation
  • Information retrieval
  • Link similarity

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