COMER: Cloud-based medicine recommendation

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.

Energy consumption optimization management mechanism based on drug green crowd data in biological pharmaceutical cloud environment

“…This method views a single data of one biological sample as a high-dimensional point cloud. The novel cloud-based medicine recommendation was proposed by Zhang Y et.al [7], which can recommend users with top-N related medicines according to symptoms. Hans A et.al [8] studied the various biomedical services that have been provided on cloud and made a comparative study of these cloud-based biomedical services.…”