iDoctor: Personalized and professionalized medical recommendations based on hybrid matrix factorization

Yin Zhang, Min Chen, Dijiang Huang, Di Wu, Yong Li

Research output: Contribution to journalArticlepeer-review

191 Scopus citations


Nowadays, crowd-sourced review websites provide decision support for various aspects of daily life, including shopping, local services, healthcare, etc. However, one of the most important challenges for existing healthcare review websites is the lack of personalized and professionalized guidelines for users to choose medical services. In this paper, we develop a novel healthcare recommendation system called iDoctor, which is based on hybrid matrix factorization methods. iDoctor differs from previous work in the following aspects: (1) emotional offset of user reviews can be unveiled by sentiment analysis and be utilized to revise original user ratings; (2) user preference and doctor feature are extracted by Latent Dirichlet Allocation and incorporated into conventional matrix factorization. We compare iDoctor with previous healthcare recommendation methods using real datasets. The experimental results show that iDoctor provides a higher predication rating and increases the accuracy of healthcare recommendation significantly.

Original languageEnglish (US)
Pages (from-to)30-35
Number of pages6
JournalFuture Generation Computer Systems
StatePublished - Jan 1 2017


  • Healthcare
  • Matrix factorization
  • Recommendation
  • Sentiment analysis
  • Topic model

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications


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