iDoctor: Personalized and professionalized medical recommendations based on hybrid matrix factorization
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- 作者：Yin Zhanga ; yin.zhang.cn@ieee.org" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Min Chenb ; minchen2012@hust.edu.cn" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Dijiang Huangc ; Dijiang.Huang@asu.edu" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Di Wud ; wudi27@sysu.edu.cn" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Yong Lie ; liyong07@tsinghua.edu.cn" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae
- 关键词：Healthcare ; Recommendation ; Matrix factorization ; Sentiment analysis ; Topic model
- 刊名：Future Generation Computer Systems
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：66
- 期：Complete
- 页码：30-35
- 全文大小：1390 K
文摘
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We propose a topic model based approach to discover user preference distribution and doctor feature distribution.
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We propose an emotion-aware approach to identify emotional offset in user reviews via sentiment analysis.
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We incorporate topic model and emotional offset into the matrix factorization model.
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The experimental results show that the proposed model provides a high-performance healthcare recommendation.
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