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

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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.

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Notes

  1. http://www.tmall.com.

  2. www.walgreens.com.

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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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Authors and Affiliations

  1. Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an, China

    Fei Hao & Xiaoming Wang

  2. School of Computer Science, Shaanxi Normal University, Xi’an, China

    Fei Hao & Xiaoming Wang

  3. Department of Computer Software Engineering, Soonchunhyang University, Asan, South Korea

    Doo-Soon Park

  4. School of Information Science and Technology, Northwest University, Xi’an, China

    Xiaoyan Yin

  5. Department of Computer Science and Engineering, Soonchunhyang University, Asan, South Korea

    Vilakone Phonexay

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  1. Fei Hao
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  2. Doo-Soon Park
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  3. Xiaoyan Yin
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  4. Xiaoming Wang
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Correspondence to Doo-Soon Park.

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Hao, F., Park, DS., Yin, X. et al. A location-sensitive over-the-counter medicines recommender based on tensor decomposition. J Supercomput 75, 1953–1970 (2019). https://doi.org/10.1007/s11227-018-2314-9

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