TY - JOUR
T1 - iDoctor
T2 - Personalized and professionalized medical recommendations based on hybrid matrix factorization
AU - Zhang, Yin
AU - Chen, Min
AU - Huang, Dijiang
AU - Wu, Di
AU - Li, Yong
N1 - Funding Information:
The research reported here was supported by China National Natural Science Foundation under Grants 61572220 . Prof. Di Wu’s work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61272397 and 61572538 , and the Guangdong Natural Science Funds for Distinguished Young Scholar under Grant No. S20120011187 .
Funding Information:
Dijiang Huang received his Bachelor of Science degree in Telecommunications from Beijing University of Posts & Telecommunications in 1995. He received his Master of Science and Ph.D. degrees from University of Missouri-Kansas City in 2001 and 2004, respectively, both majored in Computer Science and Telecommunications. He joined Computer Science and Engineering Department at ASU in 2005. Dr. Huang’s research is supported by NSF, ONR, ARO, HP, and Consortium of Embedded System (CES). He is a recipient of ONR Young Investigator Award and HP Innovative Research Award.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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.
AB - 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.
KW - Healthcare
KW - Matrix factorization
KW - Recommendation
KW - Sentiment analysis
KW - Topic model
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U2 - 10.1016/j.future.2015.12.001
DO - 10.1016/j.future.2015.12.001
M3 - Article
AN - SCOPUS:84955576796
SN - 0167-739X
VL - 66
SP - 30
EP - 35
JO - Future Generation Computer Systems
JF - Future Generation Computer Systems
ER -