Journal
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
Volume 66, Issue -, Pages 30-35Publisher
ELSEVIER
DOI: 10.1016/j.future.2015.12.001
Keywords
Healthcare; Recommendation; Matrix factorization; Sentiment analysis; Topic model
Categories
Funding
- China National Natural Science Foundation [61572220]
- National Natural Science Foundation of China [61272397, 61572538]
- Guangdong Natural Science Funds for Distinguished Young Scholar [S20120011187]
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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.
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