A location-sensitive over-the-counter medicines recommender based on tensor decomposition

  • Fei Hao
  • Doo-Soon Park
  • Xiaoyan Yin
  • Xiaoming Wang
  • Vilakone Phonexay
Article
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Abstract

The last few decades have witnessed a steady increase in medicine prescriptions for the treatment of biometric markers rather than obvious physiological symptoms; especially, the over-the-counter (OTC) medicine experiences rated by patients have huge potential to assist people to make more appropriate decisions. The most existing researches focus on the rating prediction and recommendations in E-commerce field rather than healthcare or medical treatments. In addition, the spatial and temporal factors were not considered in their recommendation mechanisms. Toward this end, this paper propose an efficient OTC medicines recommendation strategy based on tensor decomposition. Considering the impact of regional differentiation, a third-order tensor including medicine, location, and rating is constructed. To inference the usage of a new OTC medicine in a certain location, high-order singular value decomposition is applied to the above tensor for obtaining the intelligent recommendation. In order to evaluate the effectiveness of the proposed approach, we compared the conventional collaborative filtering approach and tensor-based approach in terms of precision and recall. The experimental results demonstrate that our proposed approach is significant better than collaborative filtering approach.

Keywords

Medicines recommendation Tensor decomposition HOSVD Dimensionality reduction Regional differentiation 

Notes

Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant Nos. 61702317, 61771297) and MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-2014-0-00720) supervised by the IITP (Institute for Information & communications Technology Promotion) and the National Research Foundation of Korea (No. NRF-2017R1A2B1008421) and was also supported by the Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shaanxi Province (Grant No. 2017024) as well as the Fundamental Research Funds for the Central Universities (GK201703059, GK201802013).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Fei Hao
    • 1
    • 2
  • Doo-Soon Park
    • 3
  • Xiaoyan Yin
    • 4
  • Xiaoming Wang
    • 1
    • 2
  • Vilakone Phonexay
    • 5
  1. 1.Key Laboratory of Modern Teaching TechnologyMinistry of EducationXi’anChina
  2. 2.School of Computer ScienceShaanxi Normal UniversityXi’anChina
  3. 3.Department of Computer Software EngineeringSoonchunhyang UniversityAsanSouth Korea
  4. 4.School of Information Science and TechnologyNorthwest UniversityXi’anChina
  5. 5.Department of Computer Science and EngineeringSoonchunhyang UniversityAsanSouth Korea

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