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

Future Generation Computer Systems

Volumn 66, Issue , 2017, Pages 30-35

  Zhang, Yin ^a   Chen, Min ^b   Huang, Dijiang ^c   Wu, Di ^d   Li, Yong ^e  

^a ZHONGNAN UNIVERSITY OF ECONOMICS AND LAW   (China)

^b HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^c Arizona State University   (United States)

^d SUN YAT SEN UNIVERSITY   (China)

^e TSINGHUA UNIVERSITY   (China)

Author keywords

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

Indexed keywords

DATA MINING; DECISION SUPPORT SYSTEMS; FACTORIZATION; HEALTH CARE; STATISTICS; WEBSITES;

DECISION SUPPORTS; LATENT DIRICHLET ALLOCATION; MATRIX FACTORIZATIONS; MEDICAL SERVICES; RECOMMENDATION; RECOMMENDATION METHODS; SENTIMENT ANALYSIS; TOPIC MODELING;

MATRIX ALGEBRA;

EID: 84955576796     PISSN: 0167739X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.future.2015.12.001     Document Type: Article

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