iDoctor: Personalized and professionalized medical recommendations based on hybrid matrix factorization
iDoctor: Personalized and professionalized medical recommendations based on hybrid matrix factorization
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iDoctor：基于混合矩阵分解的个性化、专业化医疗建议
DOI：
10.1016/j.future.2015.12.001
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发表时间：
2017-01-01
影响因子：
7.5
通讯作者：
Li, Yong
中科院分区：
文献类型：
--
作者：
Zhang, Yin;Chen, Min;Li, Yong
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. (C) 2015 Elsevier B.V. All rights reserved.
