cs 16(7): e2

Research Article

COMER: ClOud-based MEdicine Recommendation

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  • @ARTICLE{10.4108/icst.qshine.2014.256542,
        author={Yin Zhang and Long Wang and Long Hu and Xiaofei Wang and Min Chen},
        title={COMER: ClOud-based MEdicine Recommendation},
        journal={EAI Endorsed Transactions on Cloud Systems},
        volume={2},
        number={7},
        publisher={IEEE},
        journal_a={CS},
        year={2014},
        month={9},
        keywords={cloud, qoe, medicine recommendation, collaborative filtering, clustering, tensor decomposition},
        doi={10.4108/icst.qshine.2014.256542}
    }
    
  • Yin Zhang
    Long Wang
    Long Hu
    Xiaofei Wang
    Min Chen
    Year: 2014
    COMER: ClOud-based MEdicine Recommendation
    CS
    IEEE
    DOI: 10.4108/icst.qshine.2014.256542
Yin Zhang1, Long Wang1, Long Hu1, Xiaofei Wang2, Min Chen1,*
  • 1: Departent of Computer Science and Technology, Huazhong University of Science and Technology
  • 2: Department of Electrical and Computer Engineering, The University of British Columbia
*Contact email: minchen2012@hust.edu.cn

Abstract

With the development of e-commerce, a growing number of people prefer to purchase medicine online for the sake of convenience. However, it is a serious issue to purchase medicine blindly without necessary medication guidance. In this paper, we propose a novel cloud-based medicine recommendation, which can recommend users with top-N related medicines according to symptoms. Firstly, we cluster the drugs into several groups according to the functional description information, and design a basic personalized medicine recommendation based on user collaborative filtering. Then, considering the shortcomings of collaborative filtering algorithm, such as computing expensive, cold start, and data sparsity, we propose a cloud-based approach for enriching end-user Quality of Experience (QoE) of medicine recommendation, by modeling and representing the relationship of the user, symptom and medicine via tensor decomposition. Finally, the proposed approach is evaluated with experimental study based on a real dataset crawled from Internet.