期刊
MOBILE NETWORKS & APPLICATIONS
卷 20, 期 3, 页码 348-355出版社
SPRINGER
DOI: 10.1007/s11036-014-0537-4
关键词
Cloud; QoE; Drug recommendation; Collaborative filtering; Clustering; Tensor decomposition
类别
资金
- Deanship of Scientific Research at King Saud University [RGP-VPP-258]
- National Natural Science Foundation of China [61103185, 61472283]
- Fok Ying-Tong Education Foundation, China [142006]
- Fundamental Research Funds for the Central Universities [2100219043]
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-assisted drug recommendation (CADRE), which can recommend users with top-N related medicines according to symptoms. In CADRE, we first cluster the drugs into several groups according to the functional description information, and design a basic personalized drug 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-assisted approach for enriching end-user Quality of Experience (QoE) of drug 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.
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