Research Article
COMER: ClOud-based MEdicine Recommendation
@INPROCEEDINGS{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}, proceedings={10th International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness}, publisher={IEEE}, proceedings_a={QSHINE}, 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
QSHINE
IEEE
DOI: 10.4108/icst.qshine.2014.256542
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.
